Federated-Learning-on-Graph-and-Tabular-Data

Table of Contents

Papers
- FL on Graph Data and Graph Neural Networks
- FL on Tabular Data
- FL in top-tier journal
- FL in top-tier conference and journal by category
  - AI ML DM Secure CV NLP IR DB Network System
Framework
Datasets
Tutorials
Key Conferences/Workshops/Journals
- Workshops Special Issues Special Tracks

Papers

Categories

Artificial Intelligence (IJCAI, AAAI, AISTATS)
Machine Learning (NeurIPS, ICML, ICLR, COLT, UAI)
Data Mining (KDD, WSDM)
Secure (S&P, CCS, USENIX Security, NDSS)
Computer Vision (ICCV, CVPR, ECCV, MM)
Natural Language Processing (ACL, EMNLP, NAACL, COLING)
Information Retrieval (SIGIR)
Database (SIGMOD, ICDE, VLDB)
Network (SIGCOMM, INFOCOM, MOBICOM, NSDI, WWW)
System (OSDI, SOSP, ISCA, MLSys, TPDS)

keywords

Statistics: 🔥 code is available & stars >= 100 | ⭐ citation >= 50 | 🎓 Top-tier venue

kg.: Knowledge Graph | data.: dataset | surv.: survey

Update log

2022/09/05 - Add some information about top journals and add TPDS papers
2022/08/31 - All papers (including 400+ papers from top conferences and top journals and 100+ papers with graph and tabular data) have been comprehensively sorted out, and information such as publication addresses, links to preprints and source codes of these papers have been compiled. The source code of 280+ papers has been obtained. We hope it can help those who use this project. 😃
2022/07/31 - Add VLDB papers
2022/07/30 - Add top-tier system conferences papers and add COLT,UAI,OSDI, SOSP, ISCA, MLSys, AISTATS,WSDM papers
2022/07/28 - Add a list of top-tier conferences papers and add IJCAI,SIGIR,SIGMOD,ICDE,WWW,SIGCOMM.INFOCOM,WWW papers
2022/07/27 - add some ECCV 2022 papers
2022/07/22 - add CVPR 2022 and MM 2020,2021 papers
2022/07/21 - give TL;DR and interpret information(解读) of papers. And add KDD 2022 papers
2022/07/15 - give a list of papers in the field of federated learning in top NLP/Secure conferences. And add ICML 2022 papers
2022/07/14 - give a list of papers in the field of federated learning in top ML/CV/AI/DM conferences from innovation-cat‘s Awesome-Federated-Machine-Learning and find 🔥 papers(code is available & stars >= 100)
2022/07/12 - added information about the last commit time of the federated learning open source framework (can be used to determine the maintenance of the code base)
2022/07/12 - give a list of papers in the field of federated learning in top journals
2022/05/25 - complete the paper and code lists of FL on tabular data and Tree algorithms
2022/05/25 - add the paper list of FL on tabular data and Tree algorithms
2022/05/24 - complete the paper and code lists of FL on graph data and Graph Neural Networks
2022/05/23 - add the paper list of FL on graph data and Graph Neural Networks
2022/05/21 - update all of Federated Learning Framework

FL on Graph Data and Graph Neural Networks

This section partially refers to DBLP search engine and repositories Awesome-Federated-Learning-on-Graph-and-GNN-papers and Awesome-Federated-Machine-Learning.

Title	Affiliation	Venue	Year	TL;DR	Materials
FedWalk: Communication Efficient Federated Unsupervised Node Embedding with Differential Privacy	SJTU	KDD 🎓	2022	FedWalk¹	[PUB.] [PDF]
FederatedScope-GNN: Towards a Unified, Comprehensive and Efficient Platform for Federated Graph Learning 🔥	Alibaba	KDD (Best Paper Award) 🎓	2022	FederatedScope-GNN ²	[PDF] Code [PUB.]
Deep Neural Network Fusion via Graph Matching with Applications to Model Ensemble and Federated Learning	SJTU	ICML 🎓	2022	GAMF ³	[PUB.] [Code]
Meta-Learning Based Knowledge Extrapolation for Knowledge Graphs in the Federated Setting `kg.`	ZJU	IJCAI 🎓	2022	MaKEr⁴	[PUB.] [PDF] [Code]
Personalized Federated Learning With a Graph	UTS	IJCAI 🎓	2022	SFL⁵	[PUB.] [PDF] [Code]
Vertically Federated Graph Neural Network for Privacy-Preserving Node Classification	ZJU	IJCAI 🎓	2022	VFGNN⁶	[PUB.] [PDF]
SpreadGNN: Decentralized Multi-Task Federated Learning for Graph Neural Networks on Molecular Data	USC	AAAI:mortar_board:	2022	SpreadGNN⁷	[PUB.] PDF [Code] [解读]
FedGraph: Federated Graph Learning with Intelligent Sampling	UoA	TPDS 🎓	2022	FedGraph⁸	[PUB.] Code [解读]
FedNI: Federated Graph Learning with Network Inpainting for Population-Based Disease Prediction	UESTC	IEEE Trans. Medical Imaging	2022	FedNI⁹	[PUB.] [PDF]
FedEgo: Privacy-preserving Personalized Federated Graph Learning with Ego-graphs	SYSU	TOIS	2022	FedEgo¹⁰	[PUB.] PDF Code
A federated graph neural network framework for privacy-preserving personalization	THU	Nature Communications	2022	FedPerGNN¹¹	[PUB.] [Code] [解读]
SemiGraphFL: Semi-supervised Graph Federated Learning for Graph Classification.		PPSN	2022		[PUB.]
Efficient Federated Learning on Knowledge Graphs via Privacy-preserving Relation Embedding Aggregation `kg.`	Lehigh University	ACL Workshop	2022	FedR¹²	[PDF] [Code]
Power Allocation for Wireless Federated Learning using Graph Neural Networks		ICASSP	2022		[PUB.] [PDF] [Code]
Privacy-Preserving Federated Multi-Task Linear Regression: A One-Shot Linear Mixing Approach Inspired By Graph Regularization		ICASSP	2022		[PUB.]
Federated knowledge graph completion via embedding-contrastive learning `kg.`		Knowl. Based Syst.	2022		[PUB.]
Federated Graph Learning with Periodic Neighbour Sampling		IWQoS	2022		[PUB.]
A Privacy-Preserving Subgraph-Level Federated Graph Neural Network via Differential Privacy		KSEM	2022		[PUB.] [PDF]
Graph-Based Traffic Forecasting via Communication-Efficient Federated Learning	SUSTech	WCNC	2022	CTFL¹³	[PUB.]
Federated meta-learning for spatial-temporal prediction	NEU	Neural Comput. Appl.	2022	FML-ST¹⁴	[PUB.] Code
Malicious Transaction Identification in Digital Currency via Federated Graph Deep Learning		INFOCOM Workshops	2022		[PUB.]
Leveraging Spanning Tree to Detect Colluding Attackers in Federated Learning	Missouri S&T	INFCOM Workshops	2022	FL-ST¹⁵	[PUB.]
Federated learning of molecular properties with graph neural networks in a heterogeneous setting	University of Rochester	Patterns	2022	FLIT+¹⁶	[PUB.] PDF Code Code
Multi-Level Federated Graph Learning and Self-Attention Based Personalized Wi-Fi Indoor Fingerprint Localization	SYSU	IEEE Commun. Lett.	2022		[PUB.]
Decentralized Graph Federated Multitask Learning for Streaming Data		CISS	2022		[PUB.] [解读]
Dynamic Neural Graphs Based Federated Reptile for Semi-Supervised Multi-Tasking in Healthcare Applications		JBHI	2022		[PUB.]
FedGCN: Federated Learning-Based Graph Convolutional Networks for Non-Euclidean Spatial Data	NUIST	Mathematics	2022	FedGCN-NES¹⁷	[PUB.]
Device Sampling for Heterogeneous Federated Learning: Theory, Algorithms, and Implementation.	Purdue	INFOCOM 🎓	2021	D2D-FedL¹⁸	[PUB.] PDF
Federated Graph Classification over Non-IID Graphs	Emory	NeurIPS 🎓	2021	GCFL¹⁹	[PUB.] [PDF] [Code] [解读]
Subgraph Federated Learning with Missing Neighbor Generation	Emory; UBC; Lehigh University	NeurIPS 🎓	2021	FedSage²⁰	[PUB.] [PDF]
Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modeling	USC	KDD 🎓	2021	CNFGNN²¹	[PUB.] PDF [Code] [解读]
Differentially Private Federated Knowledge Graphs Embedding `kg.`	BUAA	CIKM	2021	FKGE²²	[PUB.] [PDF] [Code] [解读]
Differentially Private Federated Knowledge Graphs Embedding `kg.`		CIKM	2021		[PDF] [Code]
Decentralized Federated Graph Neural Networks		IJCAI Workshop	2021	D-FedGNN²³	[PDF.]
FedSGC: Federated Simple Graph Convolution for Node Classification		IJCAI Workshop	2021	FedSGC²⁴	PDF
FL-DISCO: Federated Generative Adversarial Network for Graph-based Molecule Drug Discovery: Special Session Paper	UNM	ICCAD	2021	FL-DISCO²⁵	[PUB.]
FASTGNN: A Topological Information Protected Federated Learning Approach for Traffic Speed Forecasting	UTS	IEEE Trans. Ind. Informatics		FASTGNN²⁶	[PUB.]
DAG-FL: Direct Acyclic Graph-based Blockchain Empowers On-Device Federated Learning	BUPT; UESTC	ICC	2021	DAG-FL²⁷	[PUB.] PDF
Graphical Federated Cloud Sharing Markets		TSUSC	2021		[PUB.]
Virtual Knowledge Graphs for Federated Log Analysis `kg.`		ARES	2021		[PUB.]
FedE: Embedding Knowledge Graphs in Federated Setting `kg.`		IJCKG	2021	FedE²⁸	[PUB.] [PDF] [Code]
Federated Knowledge Graph Embeddings with Heterogeneous Data `kg.`		CCKS	2021		[PUB.]
A Graph Federated Architecture with Privacy Preserving Learning	EPFL	SPAWC	2021	GFL²⁹	[PUB.] [PDF] [解读]
Federated Social Recommendation with Graph Neural Network	UIC	ACM TIST	2021	FeSoG³⁰	[PUB.] [PDF] Code
FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks 🔥 `surv.`		ICLR-DPML	2021		[PDF] [Code] [解读]
Cluster-driven Graph Federated Learning over Multiple Domains		CVPR Workshop	2021		[PDF] [解读]
Glint: Decentralized Federated Graph Learning with Traffic Throttling and Flow Scheduling		IWQoS	2021	Glint³¹	[PUB.]
A Federated Multigraph Integration Approach for Connectional Brain Template Learning		MICCAI Workshop	2021		[PDF]
Federated Graph Neural Network for Cross-graph Node Classification		CCIS	2021		[PUB.]
GraFeHTy: Graph Neural Network using Federated Learning for Human Activity Recognition		ICMLA	2021	GraFeHTy³²	[PUB.]
Distributed Training of Graph Convolutional Networks		TSIPN	2021		[PUB.] [PDF] [解读]
FedGNN: Federated Graph Neural Network for Privacy-Preserving Recommendation		ICML workshop	2021	FedGNN³³	[PDF] [解读]
Decentralized federated learning of deep neural networks on non-iid data		ICML workshop	2021	DFL-PENS³⁴	PDF PDF Code
BiG-Fed: Bilevel Optimization Enhanced Graph-Aided Federated Learning		ICML workshop	2021	BiG-Fed³⁵	[PDF]
Decentralized federated learning for electronic health records	UMN	CISS	2020	FL-DSGD³⁶	[PUB.] [解读]
ASFGNN: Automated Separated-Federated Graph Neural Network	Ant Group	PPNA	2020	ASFGNN³⁷	[PUB.] [PDF] [解读]
Decentralized federated learning via sgd over wireless d2d networks	SZU	SPAWC	2020	DSGD³⁸	[PUB.] PDF
SGNN: A Graph Neural Network Based Federated Learning Approach by Hiding Structure	SDU	BigData	2019	SGNN³⁹	[PUB.] [PDF]
Learn electronic health records by fully decentralized federated learning	UMN	NeurIPS Workshop	2019	FL-DSGD³⁶	[PDF]
Towards Federated Graph Learning for Collaborative Financial Crimes Detection		NeurIPS Workshop	2019		[PDF]
Federated learning of predictive models from federated Electronic Health Records ⭐	BU	Int. J. Medical Informatics	2018	cPDS⁴⁰	[PUB.]
Federated Graph Contrastive Learning	UTS	preprint	2022		[PDF]
Federated Graph Machine Learning: A Survey of Concepts, Techniques, and Applications `surv.`	University of Virginia	preprint	2022	FGML ⁴¹	[PDF]
FD-GATDR: A Federated-Decentralized-Learning Graph Attention Network for Doctor Recommendation Using EHR		preprint	2022	FD-GATDR⁴²	[PDF]
Privacy-preserving Graph Analytics: Secure Generation and Federated Learning		preprint	2022		[PDF]
Personalized Subgraph Federated Learning		preprint	2022		[PDF]
Federated Graph Attention Network for Rumor Detection		preprint	2022		[PDF] [Code]
FedRel: An Adaptive Federated Relevance Framework for Spatial Temporal Graph Learning		preprint	2022		[PDF]
Privatized Graph Federated Learning		preprint	2022		[PDF]
Graph-Assisted Communication-Efficient Ensemble Federated Learning		preprint	2022		[PDF]
Federated Graph Neural Networks: Overview, Techniques and Challenges `surv.`		preprint	2022		[PDF]
Decentralized event-triggered federated learning with heterogeneous communication thresholds.		preprint	2022	EF-HC⁴³	PDF
More is Better (Mostly): On the Backdoor Attacks in Federated Graph Neural Networks		preprint	2022		[PDF]
FedGCN: Convergence and Communication Tradeoffs in Federated Training of Graph Convolutional Networks		preprint	2022	FedGCN⁴⁴	[PDF] [Code]
Federated Learning with Heterogeneous Architectures using Graph HyperNetworks		preprint	2022		[PDF]
STFL: A Temporal-Spatial Federated Learning Framework for Graph Neural Networks		preprint	2021		[PDF] [Code]
Graph-Fraudster: Adversarial Attacks on Graph Neural Network Based Vertical Federated Learning		preprint	2021		[PDF] [Code]
PPSGCN: A Privacy-Preserving Subgraph Sampling Based Distributed GCN Training Method		preprint	2021	PPSGCN⁴⁵	PDF
Leveraging a Federation of Knowledge Graphs to Improve Faceted Search in Digital Libraries `kg.`		preprint	2021		[PDF]
Federated Myopic Community Detection with One-shot Communication		preprint	2021		[PDF]
Federated Graph Learning -- A Position Paper `surv.`		preprint	2021		[PDF]
A Vertical Federated Learning Framework for Graph Convolutional Network		preprint	2021	FedVGCN⁴⁶	[PDF]
FedGL: Federated Graph Learning Framework with Global Self-Supervision		preprint	2021	FedGL⁴⁷	[PDF]
FL-AGCNS: Federated Learning Framework for Automatic Graph Convolutional Network Search		preprint	2021	FL-AGCNS⁴⁸	[PDF]
Towards On-Device Federated Learning: A Direct Acyclic Graph-based Blockchain Approach		preprint	2021		[PDF]
A New Look and Convergence Rate of Federated Multi-Task Learning with Laplacian Regularization		preprint	2021	dFedU⁴⁹	PDF Code
GraphFL: A Federated Learning Framework for Semi-Supervised Node Classification on Graphs		preprint	2020	GraphFL⁵⁰	[PDF] [解读]
Improving Federated Relational Data Modeling via Basis Alignment and Weight Penalty `kg.`		preprint	2020	FedAlign-KG⁵¹	[PDF]
Federated Dynamic GNN with Secure Aggregation		preprint	2020		[PDF]
GraphFederator: Federated Visual Analysis for Multi-party Graphs		preprint	2020		[PDF]
Privacy-Preserving Graph Neural Network for Node Classification		preprint	2020		[PDF]
Peer-to-peer federated learning on graphs	University of California	preprint	2019	P2P-FLG⁵²	[PDF] [解读]

Private Graph Neural Networks (todo)

[Arxiv 2021] Privacy-Preserving Graph Convolutional Networks for Text Classification. [PDF]
[Arxiv 2021] GraphMI: Extracting Private Graph Data from Graph Neural Networks. [PDF]
[Arxiv 2021] Towards Representation Identical Privacy-Preserving Graph Neural Network via Split Learning. [PDF]
[Arxiv 2020] Locally Private Graph Neural Networks. [PDF]

FL on Tabular Data

This section refers to DBLP search engine.

Title	Affiliation	Venue	Year	TL;DR	Materials
Federated Functional Gradient Boosting	University of Pennsylvania	AISTATS 🎓	2022	FFGB⁵³	[PUB.] [PDF] [Code]
Federated Random Forests can improve local performance of predictive models for various healthcare applications	University of Marburg	Bioinform.	2022	FRF⁵⁴	[PUB.] [Code]
Federated Forest	JD	TBD	2022	FF⁵⁵	[PUB.] [PDF]
Fed-GBM: a cost-effective federated gradient boosting tree for non-intrusive load monitoring	The University of Sydney	e-Energy	2022	Fed-GBM⁵⁶	[PUB.]
eFL-Boost: Efficient Federated Learning for Gradient Boosting Decision Trees	kobe-u	IEEE Access	2022	eFL-Boost ⁵⁷	[PUB.]
Random Forest Based on Federated Learning for Intrusion Detection	Malardalen University	AIAI	2022	FL-RF⁵⁸	[PUB.]
Cross-silo federated learning based decision trees	ETH Zürich	SAC	2022	FL-DT⁵⁹	[PUB.]
Leveraging Spanning Tree to Detect Colluding Attackers in Federated Learning	Missouri S&T	INFCOM Workshops	2022	FL-ST¹⁵	[PUB.]
VF2Boost: Very Fast Vertical Federated Gradient Boosting for Cross-Enterprise Learning	PKU	SIGMOD 🎓	2021	VF2Boost⁶⁰	[PUB.]
A Blockchain-Based Federated Forest for SDN-Enabled In-Vehicle Network Intrusion Detection System		IEEE Access	2021		[PUB.]
Research on privacy protection of multi source data based on improved gbdt federated ensemble method with different metrics		Phys. Commun.	2021		[PUB.]
Fed-EINI: An Efficient and Interpretable Inference Framework for Decision Tree Ensembles in Vertical Federated Learning	UCAS; CAS	IEEE BigData	2021	Fed-EINI⁶¹	[PUB.] [PDF]
Gradient Boosting Forest: a Two-Stage Ensemble Method Enabling Federated Learning of GBDTs	THU	ICONIP	2021	GBF-Cen⁶²	[PUB.]
A k-Anonymised Federated Learning Framework with Decision Trees		DPM/CBT @ESORICS	2021		[PUB.]
AF-DNDF: Asynchronous Federated Learning of Deep Neural Decision Forests	Chalmers	SEAA	2021	AF-DNDF⁶³	[PUB.]
Compression Boosts Differentially Private Federated Learning		EuroS&P	2021		[PUB.] [PDF]
Practical Federated Gradient Boosting Decision Trees	NUS; UWA	AAAI 🎓	2020	SimFL⁶⁴	[PUB.] [PDF] [Code]
Privacy Preserving Vertical Federated Learning for Tree-based Models	NUS	VLDB 🎓	2020	Pivot-DT⁶⁵	[PUB.] PDF [Video] Code
Boosting Privately: Federated Extreme Gradient Boosting for Mobile Crowdsensing		ICDCS	2020		[PUB.] [PDF]
FedCluster: Boosting the Convergence of Federated Learning via Cluster-Cycling		IEEE BigData	2020	FedCluster⁶⁶	[PUB.] [PDF]
New Approaches to Federated XGBoost Learning for Privacy-Preserving Data Analysis	kobe-u	ICONIP	2020	FL-XGBoost⁶⁷	[PUB.]
Bandwidth Slicing to Boost Federated Learning Over Passive Optical Networks		IEEE Communications Letters	2020		[PUB.]
DFedForest: Decentralized Federated Forest		Blockchain	2020		[PUB.]
Straggler Remission for Federated Learning via Decentralized Redundant Cayley Tree		LATINCOM	2020		[PUB.]
Federated Soft Gradient Boosting Machine for Streaming Data		Federated Learning	2020		[PUB.]
Federated Learning of Deep Neural Decision Forests		LOD	2019		[PUB.]
Statistical Detection of Adversarial examples in Blockchain-based Federated Forest In-vehicle Network Intrusion Detection Systems		preprint	2022		[PDF]
Hercules: Boosting the Performance of Privacy-preserving Federated Learning		preprint	2022	Hercules⁶⁸	[PDF]
FedGBF: An efficient vertical federated learning framework via gradient boosting and bagging		preprint	2022	FedGBF⁶⁹	[PDF]
A Fair and Efficient Hybrid Federated Learning Framework based on XGBoost for Distributed Power Prediction.	THU	preprint	2022	HFL-XGBoost⁷⁰	PDF
An Efficient and Robust System for Vertically Federated Random Forest		preprint	2022		[PDF]
Efficient Batch Homomorphic Encryption for Vertically Federated XGBoost.	BUAA	preprint	2021	EBHE-VFXGB⁷¹	PDF
Guess what? You can boost Federated Learning for free		preprint	2021		[PDF]
SecureBoost+ : A High Performance Gradient Boosting Tree Framework for Large Scale Vertical Federated Learning 🔥		preprint	2021	SecureBoost+⁷²	[PDF] [Code]
Fed-TGAN: Federated Learning Framework for Synthesizing Tabular Data		preprint	2021	Fed-TGAN⁷³	[PDF]
FedXGBoost: Privacy-Preserving XGBoost for Federated Learning	TUM	preprint	2021	FedXGBoost⁷⁴	PDF
An Efficient Learning Framework For Federated XGBoost Using Secret Sharing And Distributed Optimization.	Tongji University	preprint	2021	MP-FedXGB⁷⁵	PDF Code
A Tree-based Federated Learning Approach for Personalized Treatment Effect Estimation from Heterogeneous Data Sources		preprint	2021		[PDF] [Code]
Adaptive Histogram-Based Gradient Boosted Trees for Federated Learning		preprint	2020		[PDF]
FederBoost: Private Federated Learning for GBDT	ZJU	preprint	2020	FederBoost ⁷⁶	[PDF]
Privacy Preserving Text Recognition with Gradient-Boosting for Federated Learning		preprint	2020		[PDF] [Code]
Cloud-based Federated Boosting for Mobile Crowdsensing		preprint	2020		[arxiv]
Federated Extra-Trees with Privacy Preserving		preprint	2020		[PDF]
Bandwidth Slicing to Boost Federated Learning in Edge Computing		preprint	2019		[PDF]
Revocable Federated Learning: A Benchmark of Federated Forest		preprint	2019		[PDF]
The Tradeoff Between Privacy and Accuracy in Anomaly Detection Using Federated XGBoost	CUHK	preprint	2019	F-XGBoost⁷⁷	PDF Code

FL in top-tier journal

List of papers in the field of federated learning in Nature(and its sub-journals), Cell, Science(and Science Advances) and PANS refers to WOS search engine.

Title	Affiliation	Venue	Year	TL;DR	Materials
Shifting machine learning for healthcare from development to deployment and from models to data		Nat. Biomed. Eng.	2022		[PUB.]
A federated graph neural network framework for privacy-preserving personalization	THU	Nat. Commun.	2022	FedPerGNN¹¹	[PUB.] [Code] [解读]
Communication-efficient federated learning via knowledge distillation		Nat. Commun.	2022		[PUB.] PDF Code Code
Lead federated neuromorphic learning for wireless edge artificial intelligence		Nat. Commun.	2022		[PUB.] Code [解读]
Advancing COVID-19 diagnosis with privacy-preserving collaboration in artificial intelligence		Nat. Mach. Intell.	2021		[PUB.] PDF Code
Federated learning for predicting clinical outcomes in patients with COVID-19		Nat. Med.	2021		[PUB.] Code
Adversarial interference and its mitigations in privacy-preserving collaborative machine learning		Nat. Mach. Intell.	2021		[PUB.]
Swarm Learning for decentralized and confidential clinical machine learning ⭐		Nature 🎓	2021		[PUB.] Code Software [解读]
End-to-end privacy preserving deep learning on multi-institutional medical imaging		Nat. Mach. Intell.	2021		[PUB.] Code Code [解读]
Communication-efficient federated learning		PANS.	2021		[PUB.] [Code] Code
Breaking medical data sharing boundaries by using synthesized radiographs		Science. Advances.	2020		[PUB.] Code
Secure, privacy-preserving and federated machine learning in medical imaging ⭐		Nat. Mach. Intell.	2020		[PUB.]

FL in top AI conference and journal

In this section, we will summarize Federated Learning papers accepted by top AI(Artificial Intelligence) conference and journal, Including IJCAI(International Joint Conference on Artificial Intelligence), AAAI(AAAI Conference on Artificial Intelligence), AISTATS(Artificial Intelligence and Statistics).

Title	Affiliation	Venue	Year	TL;DR	Materials
Towards Understanding Biased Client Selection in Federated Learning.	CMU	AISTATS	2022		[PUB.] code
FLIX: A Simple and Communication-Efficient Alternative to Local Methods in Federated Learning	KAUST	AISTATS	2022	FLIX⁷⁸	[PUB.] PDF code
Sharp Bounds for Federated Averaging (Local SGD) and Continuous Perspective.	Stanford	AISTATS	2022		[PUB.] PDF Code
Federated Reinforcement Learning with Environment Heterogeneity.	PKU	AISTATS	2022		[PUB.] PDF Code
Federated Myopic Community Detection with One-shot Communication	Purdue	AISTATS	2022		[PUB.] PDF
Asynchronous Upper Confidence Bound Algorithms for Federated Linear Bandits.	University of Virginia	AISTATS	2022		[PUB.] PDF Code
Towards Federated Bayesian Network Structure Learning with Continuous Optimization.	CMU	AISTATS	2022		[PUB.] PDF Code
Federated Learning with Buffered Asynchronous Aggregation	Meta AI	AISTATS	2022		[PUB.] PDF video
Differentially Private Federated Learning on Heterogeneous Data.	Stanford	AISTATS	2022	DP-SCAFFOLD⁷⁹	[PUB.] PDF Code
SparseFed: Mitigating Model Poisoning Attacks in Federated Learning with Sparsification	Princeton	AISTATS	2022	SparseFed⁸⁰	[PUB.] PDF Code video
Basis Matters: Better Communication-Efficient Second Order Methods for Federated Learning	KAUST	AISTATS	2022		[PUB.] PDF
Federated Functional Gradient Boosting.	University of Pennsylvania	AISTATS	2022		[PUB.] PDF Code
QLSD: Quantised Langevin Stochastic Dynamics for Bayesian Federated Learning.	Criteo AI Lab	AISTATS	2022	QLSD⁸¹	[PUB.] PDF Code video
Meta-Learning Based Knowledge Extrapolation for Knowledge Graphs in the Federated Setting `kg.`	ZJU	IJCAI	2022	MaKEr⁴	[PUB.] [PDF] [Code]
Personalized Federated Learning With a Graph	UTS	IJCAI	2022	SFL⁵	[PUB.] [PDF] [Code]
Vertically Federated Graph Neural Network for Privacy-Preserving Node Classification	ZJU	IJCAI	2022	VFGNN⁶	[PUB.] [PDF]
Adapt to Adaptation: Learning Personalization for Cross-Silo Federated Learning		IJCAI	2022		[PUB.] PDF Code
Heterogeneous Ensemble Knowledge Transfer for Training Large Models in Federated Learning		IJCAI	2022	Fed-ET⁸²	[PUB.] PDF
Private Semi-Supervised Federated Learning.		IJCAI	2022		[PUB.]
Continual Federated Learning Based on Knowledge Distillation.		IJCAI	2022		[PUB.]
Federated Learning on Heterogeneous and Long-Tailed Data via Classifier Re-Training with Federated Features		IJCAI	2022	CReFF⁸³	[PUB.] PDF Code
Federated Multi-Task Attention for Cross-Individual Human Activity Recognition		IJCAI	2022		[PUB.]
Personalized Federated Learning with Contextualized Generalization.		IJCAI	2022		[PUB.] PDF
Shielding Federated Learning: Robust Aggregation with Adaptive Client Selection.		IJCAI	2022		[PUB.] PDF
FedCG: Leverage Conditional GAN for Protecting Privacy and Maintaining Competitive Performance in Federated Learning		IJCAI	2022	FedCG⁸⁴	[PUB.] PDF Code
FedDUAP: Federated Learning with Dynamic Update and Adaptive Pruning Using Shared Data on the Server.		IJCAI	2022	FedDUAP⁸⁵	[PUB.] PDF
Towards Verifiable Federated Learning `surv.`		IJCAI	2022		[PUB.] PDF
HarmoFL: Harmonizing Local and Global Drifts in Federated Learning on Heterogeneous Medical Images	CUHK; BUAA	AAAI	2022		[PUB.] PDF [Code] [解读]
Federated Learning for Face Recognition with Gradient Correction	BUPT	AAAI	2022		[PUB.] PDF
SpreadGNN: Decentralized Multi-Task Federated Learning for Graph Neural Networks on Molecular Data	USC	AAAI	2022	SpreadGNN⁷	[PUB.] PDF [Code] [解读]
SmartIdx: Reducing Communication Cost in Federated Learning by Exploiting the CNNs Structures	HIT; PCL	AAAI	2022	SmartIdx⁸⁶	[PUB.] Code
Bridging between Cognitive Processing Signals and Linguistic Features via a Unified Attentional Network	TJU	AAAI	2022		[PUB.] PDF
Seizing Critical Learning Periods in Federated Learning	SUNY-Binghamton University	AAAI	2022	FedFIM⁸⁷	[PUB.] PDF
Coordinating Momenta for Cross-silo Federated Learning	University of Pittsburgh	AAAI	2022		[PUB.] PDF
FedProto: Federated Prototype Learning over Heterogeneous Devices	UTS	AAAI	2022	FedProto⁸⁸	[PUB.] PDF [Code]
FedSoft: Soft Clustered Federated Learning with Proximal Local Updating	CMU	AAAI	2022	FedSoft⁸⁹	[PUB.] PDF Code
Federated Dynamic Sparse Training: Computing Less, Communicating Less, Yet Learning Better	The University of Texas at Austin	AAAI	2022		[PUB.] PDF [Code]
FedFR: Joint Optimization Federated Framework for Generic and Personalized Face Recognition	National Taiwan University	AAAI	2022	FedFR⁹⁰	[PUB.] PDF [Code]
SplitFed: When Federated Learning Meets Split Learning	CSIRO	AAAI	2022	SplitFed⁹¹	[PUB.] PDF [Code]
Efficient Device Scheduling with Multi-Job Federated Learning	Soochow University	AAAI	2022		[PUB.] PDF
Implicit Gradient Alignment in Distributed and Federated Learning	IIT Kanpur	AAAI	2022		[PUB.] PDF
Federated Nearest Neighbor Classification with a Colony of Fruit-Flies	IBM Research	AAAI	2022	FlyNNFL⁹²	[PUB.] PDF Code
Federated Learning with Sparsification-Amplified Privacy and Adaptive Optimization		IJCAI	2021		[PUB.] PDF Video
Behavior Mimics Distribution: Combining Individual and Group Behaviors for Federated Learning		IJCAI	2021		[PUB.] PDF
FedSpeech: Federated Text-to-Speech with Continual Learning		IJCAI	2021	FedSpeech⁹³	[PUB.] PDF
Practical One-Shot Federated Learning for Cross-Silo Setting		IJCAI	2021	FedKT⁹⁴	[PUB.] PDF Code
Federated Model Distillation with Noise-Free Differential Privacy		IJCAI	2021	FEDMD-NFDP⁹⁵	[PUB.] PDF Video
LDP-FL: Practical Private Aggregation in Federated Learning with Local Differential Privacy		IJCAI	2021	LDP-FL⁹⁶	[PUB.] PDF
Federated Learning with Fair Averaging. 🔥		IJCAI	2021	FedFV⁹⁷	[PUB.] PDF Code
H-FL: A Hierarchical Communication-Efficient and Privacy-Protected Architecture for Federated Learning.		IJCAI	2021	H-FL⁹⁸	[PUB.] PDF
Communication-efficient and Scalable Decentralized Federated Edge Learning.		IJCAI	2021		[PUB.]
Secure Bilevel Asynchronous Vertical Federated Learning with Backward Updating	Xidian University; JD Tech	AAAI	2021		[PUB.] PDF video
FedRec++: Lossless Federated Recommendation with Explicit Feedback	SZU	AAAI	2021	FedRec++⁹⁹	[PUB.] video
Federated Multi-Armed Bandits	University of Virginia	AAAI	2021		[PUB.] PDF [Code] video
On the Convergence of Communication-Efficient Local SGD for Federated Learning	Temple University; University of Pittsburgh	AAAI	2021		[PUB.] video
FLAME: Differentially Private Federated Learning in the Shuffle Model	Renmin University of China; Kyoto University	AAAI	2021	FLAME_D¹⁰⁰	[PUB.] PDF video [Code]
Toward Understanding the Influence of Individual Clients in Federated Learning	SJTU; The University of Texas at Dallas	AAAI	2021		[PUB.] PDF video
Provably Secure Federated Learning against Malicious Clients	Duke University	AAAI	2021		[PUB.] PDF video slides
Personalized Cross-Silo Federated Learning on Non-IID Data	Simon Fraser University; McMaster University	AAAI	2021	FedAMP¹⁰¹	[PUB.] PDF video UC.
Model-Sharing Games: Analyzing Federated Learning under Voluntary Participation	Cornell University	AAAI	2021		[PUB.] PDF [Code] video
Curse or Redemption? How Data Heterogeneity Affects the Robustness of Federated Learning	University of Nevada; IBM Research	AAAI	2021		[PUB.] PDF video
Game of Gradients: Mitigating Irrelevant Clients in Federated Learning	IIT Bombay; IBM Research	AAAI	2021		[PUB.] PDF Code video Supplementary
Federated Block Coordinate Descent Scheme for Learning Global and Personalized Models	CUHK; Arizona State University	AAAI	2021		[PUB.] PDF video [Code]
Addressing Class Imbalance in Federated Learning	Northwestern University	AAAI	2021		[PUB.] PDF video [Code] [解读]
Defending against Backdoors in Federated Learning with Robust Learning Rate	The University of Texas at Dallas	AAAI	2021		[PUB.] PDF video [Code]
Free-rider Attacks on Model Aggregation in Federated Learning	Accenture Labs	AISTAT	2021		[PUB.] PDF Code video Supplementary
Federated f-differential privacy	University of Pennsylvania	AISTAT	2021		[PUB.] [Code] video Supplementary
Federated learning with compression: Unified analysis and sharp guarantees 🔥	The Pennsylvania State University; The University of Texas at Austin	AISTAT	2021		[PUB.] PDF [Code] video Supplementary
Shuffled Model of Differential Privacy in Federated Learning	UCLA; Google	AISTAT	2021		[PUB.] video Supplementary
Convergence and Accuracy Trade-Offs in Federated Learning and Meta-Learning	Google	AISTAT	2021		[PUB.] PDF video Supplementary
Federated Multi-armed Bandits with Personalization	University of Virginia; The Pennsylvania State University	AISTAT	2021		[PUB.] PDF [Code] video Supplementary
Towards Flexible Device Participation in Federated Learning	CMU; SYSU	AISTAT	2021		[PUB.] PDF video Supplementary
Federated Meta-Learning for Fraudulent Credit Card Detection		IJCAI	2020		[PUB.] Video
A Multi-player Game for Studying Federated Learning Incentive Schemes		IJCAI	2020	FedGame¹⁰²	[PUB.] Code[解读]
Practical Federated Gradient Boosting Decision Trees	NUS; UWA	AAAI	2020	SimFL⁶⁴	[PUB.] PDF [Code]
Federated Learning for Vision-and-Language Grounding Problems	PKU; Tencent	AAAI	2020		[PUB.]
Federated Latent Dirichlet Allocation: A Local Differential Privacy Based Framework	BUAA	AAAI	2020		[PUB.]
Federated Patient Hashing	Cornell University	AAAI	2020		[PUB.]
Robust Federated Learning via Collaborative Machine Teaching	Symantec Research Labs; KAUST	AAAI	2020		[PUB.] PDF
FedVision: An Online Visual Object Detection Platform Powered by Federated Learning	WeBank	AAAI	2020		[PUB.] PDF Code
FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization	UC Santa Barbara; UT Austin	AISTAT	2020		[PUB.] PDF video Supplementary
How To Backdoor Federated Learning 🔥	Cornell Tech	AISTAT	2020		[PUB.] PDF video [Code] Supplementary
Federated Heavy Hitters Discovery with Differential Privacy	RPI; Google	AISTAT	2020		[PUB.] PDF video Supplementary
Multi-Agent Visualization for Explaining Federated Learning	WeBank	IJCAI	2019		[PUB.] Video

FL in top ML conference and journal

In this section, we will summarize Federated Learning papers accepted by top ML(machine learning) conference and journal, Including NeurIPS(Annual Conference on Neural Information Processing Systems), ICML(International Conference on Machine Learning), ICLR(International Conference on Learning Representations), COLT(Annual Conference Computational Learning Theory) and UAI(Conference on Uncertainty in Artificial Intelligence).

Title	Affiliation	Venue	Year	TL;DR	Materials
Fast Composite Optimization and Statistical Recovery in Federated Learning	SJTU	ICML	2022		[PUB.] PDF Code
Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learning	NYU	ICML	2022	PPSGD¹⁰³	[PUB.] PDF Code
The Fundamental Price of Secure Aggregation in Differentially Private Federated Learning 🔥	Stanford; Google Research	ICML	2022		[PUB.] PDF code slides
The Poisson Binomial Mechanism for Unbiased Federated Learning with Secure Aggregation	Stanford; Google Research	ICML	2022	PBM¹⁰⁴	[PUB.] PDF Code
DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse Training	USTC	ICML	2022	DisPFL¹⁰⁵	[PUB.] PDF Code
FedNew: A Communication-Efficient and Privacy-Preserving Newton-Type Method for Federated Learning	University of Oulu	ICML	2022	FedNew¹⁰⁶	[PUB.] PDF code
DAdaQuant: Doubly-adaptive quantization for communication-efficient Federated Learning	University of Cambridge	ICML	2022	DAdaQuant¹⁰⁷	[PUB.] PDF slides Code
Accelerated Federated Learning with Decoupled Adaptive Optimization	Auburn University	ICML	2022		[PUB.] PDF
Federated Reinforcement Learning: Linear Speedup Under Markovian Sampling	Georgia Tech	ICML	2022		[PUB.] PDF
Multi-Level Branched Regularization for Federated Learning	Seoul National University	ICML	2022	FedMLB¹⁰⁸	[PUB.] PDF Code Page
FedScale: Benchmarking Model and System Performance of Federated Learning at Scale 🔥	University of Michigan	ICML	2022	FedScale¹⁰⁹	[PUB.] PDF code
Federated Learning with Positive and Unlabeled Data	XJTU	ICML	2022	FedPU¹¹⁰	[PUB.] PDF Code
Deep Neural Network Fusion via Graph Matching with Applications to Model Ensemble and Federated Learning	SJTU	ICML	2022		[PUB.] code
Orchestra: Unsupervised Federated Learning via Globally Consistent Clustering	University of Michigan	ICML	2022	Orchestra¹¹¹	[PUB.] PDF code
Disentangled Federated Learning for Tackling Attributes Skew via Invariant Aggregation and Diversity Transferring	USTC	ICML	2022	DFL¹¹²	[PUB.] PDF Code slides [解读]
Architecture Agnostic Federated Learning for Neural Networks	The University of Texas at Austin	ICML	2022	FedHeNN¹¹³	[PUB.] PDF Slides
Personalized Federated Learning through Local Memorization	Inria	ICML	2022	KNN-PER¹¹⁴	[PUB.] PDF code
Proximal and Federated Random Reshuffling	KAUST	ICML	2022	ProxRR¹¹⁵	[PUB.] PDF code
Federated Learning with Partial Model Personalization	University of Washington	ICML	2022		[PUB.] PDF code
Generalized Federated Learning via Sharpness Aware Minimization	University of South Florida	ICML	2022		[PUB.] PDF
FedNL: Making Newton-Type Methods Applicable to Federated Learning	KAUST	ICML	2022	FedNL¹¹⁶	[PUB.] PDF video slides
Federated Minimax Optimization: Improved Convergence Analyses and Algorithms	CMU	ICML	2022		[PUB.] PDF slides
Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learning	Hong Kong Baptist University	ICML	2022	VFL¹¹⁷	[PUB.] PDF code [解读]
FedNest: Federated Bilevel, Minimax, and Compositional Optimization	University of Michigan	ICML	2022	FedNest¹¹⁸	[PUB.] PDF code
EDEN: Communication-Efficient and Robust Distributed Mean Estimation for Federated Learning	VMware Research	ICML	2022	EDEN¹¹⁹	[PUB.] PDF code
Communication-Efficient Adaptive Federated Learning	Pennsylvania State University	ICML	2022		[PUB.] PDF
ProgFed: Effective, Communication, and Computation Efficient Federated Learning by Progressive Training	CISPA Helmholz Center for Information Security	ICML	2022	ProgFed¹²⁰	[PUB.] PDF slides code
Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification 🔥	University of Maryland	ICML	2022	breaching¹²¹	[PUB.] PDF code
Anarchic Federated Learning	The Ohio State University	ICML	2022		[PUB.] PDF
QSFL: A Two-Level Uplink Communication Optimization Framework for Federated Learning	Nankai University	ICML	2022	QSFL¹²²	[PUB.] code
Bitwidth Heterogeneous Federated Learning with Progressive Weight Dequantization	KAIST	ICML	2022		[PUB.] PDF
Neural Tangent Kernel Empowered Federated Learning	NC State University	ICML	2022		[PUB.] PDF code
Understanding Clipping for Federated Learning: Convergence and Client-Level Differential Privacy	UMN	ICML	2022		[PUB.] PDF
Personalized Federated Learning via Variational Bayesian Inference	CAS	ICML	2022		[PUB.] PDF slides UC.
Federated Learning with Label Distribution Skew via Logits Calibration	ZJU	ICML	2022		[PUB.]
Neurotoxin: Durable Backdoors in Federated Learning	Southeast University;Princeton	ICML	2022	Neurotoxin¹²³	[PUB.] PDF code
Resilient and Communication Efficient Learning for Heterogeneous Federated Systems	Michigan State University	ICML	2022		[PUB.]
Bayesian Framework for Gradient Leakage	ETH Zurich	ICLR	2022		[PUB.] PDF [Code]
Federated Learning from only unlabeled data with class-conditional-sharing clients	The University of Tokyo; CUHK	ICLR	2022	FedUL¹²⁴	[PUB.] [Code]
FedChain: Chained Algorithms for Near-Optimal Communication Cost in Federated Learning	CMU; University of Illinois at Urbana-Champaign; University of Washington	ICLR	2022	FedChain¹²⁵	[PUB.] PDF
Acceleration of Federated Learning with Alleviated Forgetting in Local Training	THU	ICLR	2022	FedReg¹²⁶	[PUB.] PDF [Code]
FedPara: Low-rank Hadamard Product for Communicatkion-Efficient Federated Learning	POSTECH	ICLR	2022		[PUB.] PDF [Code]
An Agnostic Approach to Federated Learning with Class Imbalance	University of Pennsylvania	ICLR	2022		[PUB.] [Code]
Efficient Split-Mix Federated Learning for On-Demand and In-Situ Customization	Michigan State University; The University of Texas at Austin	ICLR	2022		[PUB.] PDF [Code]
Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models 🔥	University of Maryland; NYU	ICLR	2022		[PUB.] PDF [Code] [Code]
ZeroFL: Efficient On-Device Training for Federated Learning with Local Sparsity	University of Cambridge; University of Oxford	ICLR	2022		[PUB.] PDF
Diverse Client Selection for Federated Learning via Submodular Maximization	Intel; CMU	ICLR	2022		[PUB.] [Code]
Recycling Model Updates in Federated Learning: Are Gradient Subspaces Low-Rank?	Purdue	ICLR	2022		[PUB.] PDF [Code]
Diurnal or Nocturnal? Federated Learning of Multi-branch Networks from Periodically Shifting Distributions 🔥	University of Maryland; Google	ICLR	2022		[PUB.] [Code]
Towards Model Agnostic Federated Learning Using Knowledge Distillation	EPFL	ICLR	2022		[PUB.] PDF Code
Divergence-aware Federated Self-Supervised Learning	NTU; SenseTime	ICLR	2022		[PUB.] PDF Code
What Do We Mean by Generalization in Federated Learning? 🔥	Stanford; Google	ICLR	2022		[PUB.] PDF [Code]
FedBABU: Toward Enhanced Representation for Federated Image Classification	KAIST	ICLR	2022		[PUB.] PDF [Code]
Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing	EPFL	ICLR	2022		[PUB.] PDF [Code]
Improving Federated Learning Face Recognition via Privacy-Agnostic Clusters	Aibee	ICLR Spotlight	2022		[PUB.] PDF Page [解读]
Hybrid Local SGD for Federated Learning with Heterogeneous Communications	University of Texas; Pennsylvania State University	ICLR	2022		[PUB.]
On Bridging Generic and Personalized Federated Learning for Image Classification	The Ohio State University	ICLR	2022	Fed-RoD¹²⁷	[PUB.] PDF [Code]
Minibatch vs Local SGD with Shuffling: Tight Convergence Bounds and Beyond	KAIST; MIT	ICLR	2022		[PUB.] PDF
Constrained differentially private federated learning for low-bandwidth devices		UAI	2021		[PUB.] PDF
Federated stochastic gradient Langevin dynamics		UAI	2021		[PUB.] PDF
Federated Learning Based on Dynamic Regularization	BU; ARM	ICLR	2021		[PUB.] PDF Code
Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learning	The Ohio State University	ICLR	2021		[PUB.] PDF
HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients	Duke University	ICLR	2021	HeteroFL¹²⁸	[PUB.] PDF [Code]
FedMix: Approximation of Mixup under Mean Augmented Federated Learning	KAIST	ICLR	2021	FedMix¹²⁹	[PUB.] PDF
Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms 🔥	CMU; Google	ICLR	2021		[PUB.] PDF [Code]
Adaptive Federated Optimization 🔥	Google	ICLR	2021		[PUB.] PDF [Code]
Personalized Federated Learning with First Order Model Optimization	Stanford; NVIDIA	ICLR	2021	FedFomo¹³⁰	[PUB.] PDF Code UC.
FedBN: Federated Learning on Non-IID Features via Local Batch Normalization 🔥	Princeton	ICLR	2021	FedBN¹³¹	[PUB.] PDF [Code]
FedBE: Making Bayesian Model Ensemble Applicable to Federated Learning	The Ohio State University	ICLR	2021	FedBE¹³²	[PUB.] PDF Code
Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learning	KAIST	ICLR	2021		[PUB.] PDF [Code]
KD3A: Unsupervised Multi-Source Decentralized Domain Adaptation via Knowledge Distillation	ZJU	ICML	2021		[PUB.] PDF Code [解读]
Gradient Disaggregation: Breaking Privacy in Federated Learning by Reconstructing the User Participant Matrix	Harvard University	ICML	2021		[PUB.] PDF video [Code]
FL-NTK: A Neural Tangent Kernel-based Framework for Federated Learning Analysis	PKU; Princeton	ICML	2021	FL-NTK¹³³	[PUB.] PDF video
Personalized Federated Learning using Hypernetworks 🔥	Bar-Ilan University; NVIDIA	ICML	2021		[PUB.] PDF [Code] Page video [解读]
Federated Composite Optimization	Stanford; Google	ICML	2021		[PUB.] PDF [Code] video slides
Exploiting Shared Representations for Personalized Federated Learning	University of Texas at Austin; University of Pennsylvania	ICML	2021		[PUB.] PDF [Code] video
Data-Free Knowledge Distillation for Heterogeneous Federated Learning 🔥	Michigan State University	ICML	2021		[PUB.] PDF [Code] video
Federated Continual Learning with Weighted Inter-client Transfer	KAIST	ICML	2021		[PUB.] PDF [Code] video
Federated Deep AUC Maximization for Hetergeneous Data with a Constant Communication Complexity	The University of Iowa	ICML	2021		[PUB.] PDF Code Code video
Bias-Variance Reduced Local SGD for Less Heterogeneous Federated Learning	The University of Tokyo	ICML	2021		[PUB.] PDF video
Federated Learning of User Verification Models Without Sharing Embeddings	Qualcomm	ICML	2021		[PUB.] PDF video
Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learning	Accenture	ICML	2021		[PUB.] PDF [Code] video
Ditto: Fair and Robust Federated Learning Through Personalization	CMU; Facebook AI	ICML	2021		[PUB.] PDF [Code] video
Heterogeneity for the Win: One-Shot Federated Clustering	CMU	ICML	2021		[PUB.] PDF video
The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation 🔥	Google	ICML	2021		[PUB.] PDF CODE video
Debiasing Model Updates for Improving Personalized Federated Training	BU; Arm	ICML	2021		[PUB.] Code video
One for One, or All for All: Equilibria and Optimality of Collaboration in Federated Learning	Toyota; Berkeley; Cornell University	ICML	2021		[PUB.] PDF [Code] video
CRFL: Certifiably Robust Federated Learning against Backdoor Attacks	UIUC; IBM	ICML	2021		[PUB.] PDF [Code] video
Federated Learning under Arbitrary Communication Patterns	Indiana University; Amazon	ICML	2021		[PUB.] video
Sageflow: Robust Federated Learning against Both Stragglers and Adversaries	KAIST	NeurIPS	2021	Sageflow¹³⁴	[PUB.]
CAFE: Catastrophic Data Leakage in Vertical Federated Learning	Rensselaer Polytechnic Institute; IBM Research	NeurIPS	2021	CAFE¹³⁵	[PUB.] [Code]
Fault-Tolerant Federated Reinforcement Learning with Theoretical Guarantee	NUS	NeurIPS	2021		[PUB.] PDF [Code]
Optimality and Stability in Federated Learning: A Game-theoretic Approach	Cornell University	NeurIPS	2021		[PUB.] PDF [Code]
QuPeD: Quantized Personalization via Distillation with Applications to Federated Learning	UCLA	NeurIPS	2021	QuPeD¹³⁶	[PUB.] PDF [Code] [解读]
The Skellam Mechanism for Differentially Private Federated Learning 🔥	Google Research; CMU	NeurIPS	2021		[PUB.] PDF Code
No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data	NUS; Huawei	NeurIPS	2021		[PUB.] PDF
STEM: A Stochastic Two-Sided Momentum Algorithm Achieving Near-Optimal Sample and Communication Complexities for Federated Learning	UMN	NeurIPS	2021		[PUB.] PDF
Subgraph Federated Learning with Missing Neighbor Generation	Emory; UBC; Lehigh University	NeurIPS	2021	FedSage²⁰	[PUB.] PDF Code [解读]
Evaluating Gradient Inversion Attacks and Defenses in Federated Learning 🔥	Princeton	NeurIPS	2021	GradAttack¹³⁷	[PUB.] PDF Code
Personalized Federated Learning With Gaussian Processes	Bar-Ilan University	NeurIPS	2021		[PUB.] PDF [Code]
Differentially Private Federated Bayesian Optimization with Distributed Exploration	MIT; NUS	NeurIPS	2021		[PUB.] PDF [Code]
Parameterized Knowledge Transfer for Personalized Federated Learning	PolyU	NeurIPS	2021		[PUB.] PDF
Federated Reconstruction: Partially Local Federated Learning 🔥	Google Research	NeurIPS	2021		[PUB.] PDF Code UC.
Fast Federated Learning in the Presence of Arbitrary Device Unavailability	THU; Princeton; MIT	NeurIPS	2021		[PUB.] PDF [Code]
FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client Perspective	Duke University; Accenture Labs	NeurIPS	2021	FL-WBC¹³⁸	[PUB.] PDF [Code]
FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout	KAUST; Samsung AI Center	NeurIPS	2021	FjORD¹³⁹	[PUB.] PDF
Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradients	University of Pennsylvania	NeurIPS	2021		[PUB.] PDF Video
Federated Multi-Task Learning under a Mixture of Distributions	INRIA; Accenture Labs	NeurIPS	2021		[PUB.] PDF [Code]
Federated Graph Classification over Non-IID Graphs	Emory	NeurIPS	2021	GCFL¹⁹	[PUB.] PDF Code [解读] [解读]
Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing	CMU; Hewlett Packard Enterprise	NeurIPS	2021	FedEx¹⁴⁰	[PUB.] PDF [Code]
On Large-Cohort Training for Federated Learning 🔥	Google; CMU	NeurIPS	2021	Large-Cohort¹⁴¹	[PUB.] PDF [Code]
DeepReduce: A Sparse-tensor Communication Framework for Federated Deep Learning	KAUST; Columbia University; University of Central Florida	NeurIPS	2021	DeepReduce¹⁴²	[PUB.] PDF [Code]
PartialFed: Cross-Domain Personalized Federated Learning via Partial Initialization	Huawei	NeurIPS	2021	PartialFed¹⁴³	[PUB.] Video
Federated Split Task-Agnostic Vision Transformer for COVID-19 CXR Diagnosis	KAIST	NeurIPS	2021		[PUB.] PDF
Addressing Algorithmic Disparity and Performance Inconsistency in Federated Learning	THU; Alibaba; Weill Cornell Medicine	NeurIPS	2021	FCFL[^FCFL ]	[PUB.] PDF [Code]
Federated Linear Contextual Bandits	The Pennsylvania State University; Facebook; University of Virginia	NeurIPS	2021		[PUB.] PDF Code
Few-Round Learning for Federated Learning	KAIST	NeurIPS	2021		[PUB.]
Breaking the centralized barrier for cross-device federated learning	EPFL; Google Research	NeurIPS	2021		[PUB.] [Code] Video
Federated-EM with heterogeneity mitigation and variance reduction	Ecole Polytechnique; Google Research	NeurIPS	2021	Federated-EM¹⁴⁴	[PUB.] PDF
Delayed Gradient Averaging: Tolerate the Communication Latency for Federated Learning	MIT; Amazon; Google	NeurIPS	2021		[PUB.] Page Slides
FedDR – Randomized Douglas-Rachford Splitting Algorithms for Nonconvex Federated Composite Optimization	University of North Carolina at Chapel Hill; IBM Research	NeurIPS	2021	FedDR¹⁴⁵	[PUB.] PDF [Code]
Gradient Inversion with Generative Image Prior	Pohang University of Science and Technology; University of Wisconsin-Madison; University of Washington	NeurIPS	2021		[PUB.] PDF [Code]
Federated Adversarial Domain Adaptation	BU; Columbia University; Rutgers University	ICLR	2020		[PUB.] PDF Code
DBA: Distributed Backdoor Attacks against Federated Learning	ZJU; IBM Research	ICLR	2020		[PUB.] [Code]
Fair Resource Allocation in Federated Learning 🔥	CMU; Facebook AI	ICLR	2020	fair-flearn¹⁴⁶	[PUB.] PDF [Code]
Federated Learning with Matched Averaging 🔥	University of Wisconsin-Madison; IBM Research	ICLR	2020	FedMA¹⁴⁷	[PUB.] PDF [Code]
Differentially Private Meta-Learning	CMU	ICLR	2020		[PUB.] PDF
Generative Models for Effective ML on Private, Decentralized Datasets 🔥	Google	ICLR	2020		[PUB.] PDF [Code]
On the Convergence of FedAvg on Non-IID Data 🔥	PKU	ICLR	2020		[PUB.] PDF [Code] [解读]
FedBoost: A Communication-Efficient Algorithm for Federated Learning	Google	ICML	2020	FedBoost¹⁴⁸	[PUB.] Video
FetchSGD: Communication-Efficient Federated Learning with Sketching	UC Berkeley; Johns Hopkins University; Amazon	ICML	2020	FetchSGD¹⁴⁹	[PUB.] PDF Video [Code]
SCAFFOLD: Stochastic Controlled Averaging for Federated Learning	EPFL; Google	ICML	2020	SCAFFOLD¹⁵⁰	[PUB.] PDF Video UC. UC. [解读]
Federated Learning with Only Positive Labels	Google	ICML	2020		[PUB.] PDF Video
From Local SGD to Local Fixed-Point Methods for Federated Learning	Moscow Institute of Physics and Technology; KAUST	ICML	2020		[PUB.] PDF Slides Video
Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization	KAUST	ICML	2020		[PUB.] PDF Slide Video
Differentially-Private Federated Linear Bandits	MIT	NeurIPS	2020		[PUB.] PDF [Code]
Federated Principal Component Analysis	University of Cambridge; Quine Technologies	NeurIPS	2020		[PUB.] PDF [Code]
FedSplit: an algorithmic framework for fast federated optimization	UC Berkeley	NeurIPS	2020	FedSplit¹⁵¹	[PUB.] PDF
Federated Bayesian Optimization via Thompson Sampling	NUS; MIT	NeurIPS	2020	fbo¹⁵²	[PUB.] PDF Code
Lower Bounds and Optimal Algorithms for Personalized Federated Learning	KAUST	NeurIPS	2020		[PUB.] PDF
Robust Federated Learning: The Case of Affine Distribution Shifts	UC Santa Barbara; MIT	NeurIPS	2020	RobustFL¹⁵³	[PUB.] PDF Code
An Efficient Framework for Clustered Federated Learning	UC Berkeley; DeepMind	NeurIPS	2020	ifca[^ifca]	[PUB.] PDF [Code]
Distributionally Robust Federated Averaging 🔥	Pennsylvania State University	NeurIPS	2020	DRFA¹⁵⁴	[PUB.] PDF [Code]
Personalized Federated Learning with Moreau Envelopes 🔥	The University of Sydney	NeurIPS	2020		[PUB.] PDF [Code]
Personalized Federated Learning with Theoretical Guarantees: A Model-Agnostic Meta-Learning Approach	MIT; UT Austin	NeurIPS	2020	Per-FedAvg¹⁵⁵	[PUB.] PDF UC. UC.
Group Knowledge Transfer: Federated Learning of Large CNNs at the Edge	USC	NeurIPS	2020	FedGKT¹⁵⁶	[PUB.] PDF [Code] [解读]
Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization 🔥	CMU; Princeton	NeurIPS	2020	FedNova¹⁵⁷	[PUB.] PDF Code UC.
Attack of the Tails: Yes, You Really Can Backdoor Federated Learning	University of Wisconsin-Madison	NeurIPS	2020		[PUB.] PDF
Federated Accelerated Stochastic Gradient Descent	Stanford	NeurIPS	2020	FedAc¹⁵⁸	[PUB.] PDF [Code] Video
Inverting Gradients - How easy is it to break privacy in federated learning? 🔥	University of Siegen	NeurIPS	2020		[PUB.] PDF [Code]
Ensemble Distillation for Robust Model Fusion in Federated Learning	EPFL	NeurIPS	2020	FedDF¹⁵⁹	[PUB.] PDF Code
Throughput-Optimal Topology Design for Cross-Silo Federated Learning	INRIA	NeurIPS	2020		[PUB.] PDF [Code]
Bayesian Nonparametric Federated Learning of Neural Networks 🔥	IBM	ICML	2019		[PUB.] PDF [Code]
Analyzing Federated Learning through an Adversarial Lens 🔥	Princeton; IBM	ICML	2019		[PUB.] PDF [Code]
Agnostic Federated Learning	Google	ICML	2019		[PUB.] PDF
cpSGD: Communication-efficient and differentially-private distributed SGD	Princeton; Google	NeurIPS	2018		[PUB.] PDF
Federated Multi-Task Learning 🔥	Stanford; USC; CMU	NeurIPS	2017		[PUB.] PDF [Code]

FL in top DM conference and journal

In this section, we will summarize Federated Learning papers accepted by top DM(Data Mining) conference and journal, Including KDD(ACM SIGKDD Conference on Knowledge Discovery and Data Mining) and WSDM(Web Search and Data Mining).

KDD 2022(Research Track, Applied Data Science track) , 2021,2020
WSDM 2022, 2021, 2019

Title	Affiliation	Venue	Year	TL;DR	Materials
Collaboration Equilibrium in Federated Learning	THU	KDD	2022	CE¹⁶⁰	[PDF] [PUB.] Code
Connected Low-Loss Subspace Learning for a Personalization in Federated Learning	Ulsan National Institute of Science and Technology	KDD	2022	SuPerFed ¹⁶¹	[PDF] [PUB.] Code
FedMSplit: Correlation-Adaptive Federated Multi-Task Learning across Multimodal Split Networks	University of Virginia	KDD	2022	FedMSplit¹⁶²	[PUB.]
Communication-Efficient Robust Federated Learning with Noisy Labels	University of Pittsburgh	KDD	2022	Comm-FedBiO¹⁶³	[PDF] [PUB.]
FLDetector: Detecting Malicious Clients in Federated Learning via Checking Model-Updates Consistency	USTC	KDD	2022	FLDetector¹⁶⁴	[PDF] [PUB.] Code
Practical Lossless Federated Singular Vector Decomposition Over Billion-Scale Data	HKUST	KDD	2022	FedSVD ¹⁶⁵	[PDF] [PUB.] Code
FedWalk: Communication Efficient Federated Unsupervised Node Embedding with Differential Privacy	SJTU	KDD	2022	FedWalk¹	[PDF] [PUB.]
FederatedScope-GNN: Towards a Unified, Comprehensive and Efficient Platform for Federated Graph Learning 🔥	Alibaba	KDD (Best Paper Award)	2022	FederatedScope-GNN ²	[PDF] Code [PUB.]
Fed-LTD: Towards Cross-Platform Ride Hailing via Federated Learning to Dispatch	BUAA	KDD	2022	Fed-LTD ¹⁶⁶	[PDF] [PUB.] [解读]
Felicitas: Federated Learning in Distributed Cross Device Collaborative Frameworks	USTC	KDD	2022	Felicitas ¹⁶⁷	[PDF] [PUB.]
No One Left Behind: Inclusive Federated Learning over Heterogeneous Devices	Renmin University of China	KDD	2022	InclusiveFL ¹⁶⁸	[PDF] [PUB.]
FedAttack: Effective and Covert Poisoning Attack on Federated Recommendation via Hard Sampling	THU	KDD	2022	FedAttack ¹⁶⁹	[PDF] [PUB.] code
PipAttack: Poisoning Federated Recommender Systems for Manipulating Item Promotion	The University of Queensland	WSDM	2022	PipAttack¹⁷⁰	[PDF] [PUB.]
Fed2: Feature-Aligned Federated Learning	George Mason University; Microsoft; University of Maryland	KDD	2021	Fed2¹⁷¹	PDF [PUB.]
FedRS: Federated Learning with Restricted Softmax for Label Distribution Non-IID Data	Nanjing University	KDD	2021	FedRS¹⁷²	Code [PUB.]
Federated Adversarial Debiasing for Fair and Trasnferable Representations	Michigan State University	KDD	2021	FADE¹⁷³	Page Code Slides [PUB.]
Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modeling	USC	KDD	2021	CNFGNN²¹	[PUB.] [Code] [解读]
AsySQN: Faster Vertical Federated Learning Algorithms with Better Computation Resource Utilization	Xidian University;JD Tech	KDD	2021	AsySQN¹⁷⁴	PDF [PUB.]
FLOP: Federated Learning on Medical Datasets using Partial Networks	Duke University	KDD	2021	FLOP¹⁷⁵	PDF [PUB.] [Code]
A Practical Federated Learning Framework for Small Number of Stakeholders	ETH Zürich	WSDM	2021	Federated-Learning-source¹⁷⁶	[PUB.] Code
Federated Deep Knowledge Tracing	USTC	WSDM	2021	FDKT¹⁷⁷	[PUB.] [Code]
FedFast: Going Beyond Average for Faster Training of Federated Recommender Systems	University College Dublin	KDD	2020	FedFast¹⁷⁸	[PUB.] video
Federated Doubly Stochastic Kernel Learning for Vertically Partitioned Data	JD Tech	KDD	2020	FDSKL¹⁷⁹	[PUB.] PDF video
Federated Online Learning to Rank with Evolution Strategies	Facebook AI Research	WSDM	2019	FOLtR-ES¹⁸⁰	[PUB.] Code

FL in top Secure conference and journal

In this section, we will summarize Federated Learning papers accepted by top Secure conference and journal, Including S&P(IEEE Symposium on Security and Privacy), CCS(Conference on Computer and Communications Security), USENIX Security(Usenix Security Symposium) and NDSS(Network and Distributed System Security Symposium).

Title	Affiliation	Venue	Year	TL;DR	Materials
Private, Efficient, and Accurate: Protecting Models Trained by Multi-party Learning with Differential Privacy	Fudan University	S&P	2023	PEA¹⁸¹	[PDF]
Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learning	University of Massachusetts	S&P	2022		[PUB.] Video
SIMC: ML Inference Secure Against Malicious Clients at Semi-Honest Cost	Microsoft Research	USENIX Security	2022	SIMC¹⁸²	[PUB.] PDF code
Efficient Differentially Private Secure Aggregation for Federated Learning via Hardness of Learning with Errors	University of Vermont	USENIX Security	2022		[PUB.] Slides
Label Inference Attacks Against Vertical Federated Learning	ZJU	USENIX Security	2022		[PUB.] Slides code
FLAME: Taming Backdoors in Federated Learning	Technical University of Darmstadt	USENIX Security	2022	FLAME¹⁸³	[PUB.] Slides PDF
Local and Central Differential Privacy for Robustness and Privacy in Federated Learning	University at Buffalo, SUNY	NDSS	2022		[PUB.] PDF UC.
Interpretable Federated Transformer Log Learning for Cloud Threat Forensics	University of the Incarnate Word	NDSS	2022		[PUB.] UC.
FedCRI: Federated Mobile Cyber-Risk Intelligence	Technical University of Darmstadt	NDSS	2022	FedCRI¹⁸⁴	[PUB.]
DeepSight: Mitigating Backdoor Attacks in Federated Learning Through Deep Model Inspection	Technical University of Darmstadt	NDSS	2022	DeepSight¹⁸⁵	[PUB.] PDF
Private Hierarchical Clustering in Federated Networks	NUS	CCS	2021		[PUB.] PDF
FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping	Duke University	NDSS	2021		[PUB.] PDF code Video Slides
POSEIDON: Privacy-Preserving Federated Neural Network Learning	EPFL	NDSS	2021		[PUB.] Video
Manipulating the Byzantine: Optimizing Model Poisoning Attacks and Defenses for Federated Learning	University of Massachusetts Amherst	NDSS	2021		[PUB.] code Video
Local Model Poisoning Attacks to Byzantine-Robust Federated Learning	The Ohio State University	USENIX Security	2020		[PUB.] PDF code Video Slides
A Reliable and Accountable Privacy-Preserving Federated Learning Framework using the Blockchain	University of Kansas	CCS (Poster)	2019		[PUB.]
IOTFLA : A Secured and Privacy-Preserving Smart Home Architecture Implementing Federated Learning	Université du Québéc á Montréal	S&P （Workshop）	2019		[PUB.]
Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning 🔥	University of Massachusetts Amherst	S&P	2019		[PUB.] [PUB.] Video Video Slides code
Practical Secure Aggregation for Privacy Preserving Machine Learning	Google	CCS	2017		[PUB.] PDF [解读] UC. UC. UC.

FL in top CV conference and journal

In this section, we will summarize Federated Learning papers accepted by top CV(computer vision) conference and journal, Including CVPR(Computer Vision and Pattern Recognition), ICCV(IEEE International Conference on Computer Vision), ECCV(European Conference on Computer Vision), MM(ACM International Conference on Multimedia).

Title	Affiliation	Venue	Year	TL;DR	Materials
FedX: Unsupervised Federated Learning with Cross Knowledge Distillation	KAIST	ECCV	2022	FedX¹⁸⁶	[PUB.] [PDF] Code
Personalizing Federated Medical Image Segmentation via Local Calibration	Xiamen University	ECCV	2022	LC-Fed ¹⁸⁷	[PUB.] [PDF] Code
Improving Generalization in Federated Learning by Seeking Flat Minima	Politecnico di Torino	ECCV	2022	FedSAM ¹⁸⁸	[PUB.] [PDF] Code
ATPFL: Automatic Trajectory Prediction Model Design Under Federated Learning Framework	HIT	CVPR	2022	ATPFL ¹⁸⁹	[PUB.]
Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learning	Stanford	CVPR	2022	ViT-FL ¹⁹⁰	[PUB.] [supp] [PDF] [Code] video
FedCorr: Multi-Stage Federated Learning for Label Noise Correction	Singapore University of Technology and Design	CVPR	2022	FedCorr¹⁹¹	[PUB.] [supp] [PDF] [Code] video
FedCor: Correlation-Based Active Client Selection Strategy for Heterogeneous Federated Learning	Duke University	CVPR	2022	FedCor ¹⁹²	[PUB.] [supp] [PDF]
Layer-Wised Model Aggregation for Personalized Federated Learning	PolyU	CVPR	2022	pFedLA ¹⁹³	[PUB.] [supp] [PDF]
Local Learning Matters: Rethinking Data Heterogeneity in Federated Learning	University of Central Florida	CVPR	2022	FedAlign¹⁹⁴	[PUB.] [supp] [PDF] [Code]
Federated Learning With Position-Aware Neurons	Nanjing University	CVPR	2022	PANs ¹⁹⁵	[PUB.] [supp] [PDF]
RSCFed: Random Sampling Consensus Federated Semi-Supervised Learning	HKUST	CVPR	2022	RSCFed ¹⁹⁶	[PUB.] [supp] [PDF] [Code]
Learn From Others and Be Yourself in Heterogeneous Federated Learning	Wuhan University	CVPR	2022	FCCL¹⁹⁷	[PUB.] [code] video
Robust Federated Learning With Noisy and Heterogeneous Clients	Wuhan University	CVPR	2022	RHFL ¹⁹⁸	[PUB.] [supp] [Code]
ResSFL: A Resistance Transfer Framework for Defending Model Inversion Attack in Split Federated Learning	Arizona State University	CVPR	2022	ResSFL ¹⁹⁹	[PUB.] [supp] [PDF] [Code]
FedDC: Federated Learning With Non-IID Data via Local Drift Decoupling and Correction	National University of Defense Technology	CVPR	2022	FedDC ²⁰⁰	[PUB.] [PDF] [Code] [解读]
Federated Class-Incremental Learning	CAS; Northwestern University; UTS	CVPR	2022	GLFC ²⁰¹	[PUB.] [PDF] [Code]
Fine-Tuning Global Model via Data-Free Knowledge Distillation for Non-IID Federated Learning	PKU; JD Explore Academy; The University of Sydney	CVPR	2022	FedFTG ²⁰²	[PUB.] [PDF]
Differentially Private Federated Learning With Local Regularization and Sparsification	CAS	CVPR	2022	DP-FedAvg +BLUR + LUS ²⁰³	[PUB.] [PDF]
Auditing Privacy Defenses in Federated Learning via Generative Gradient Leakage	University of Tennessee; Oak Ridge National Laboratory; Google Research	CVPR	2022	GGL ²⁰⁴	[PUB.] [PDF] [Code] video
CD2-pFed: Cyclic Distillation-Guided Channel Decoupling for Model Personalization in Federated Learning	SJTU	CVPR	2022	CD2-pFed ²⁰⁵	[PUB.] [PDF]
Closing the Generalization Gap of Cross-Silo Federated Medical Image Segmentation	Univ. of Pittsburgh; NVIDIA	CVPR	2022	FedSM ²⁰⁶	[PUB.] [PDF]
Multi-Institutional Collaborations for Improving Deep Learning-Based Magnetic Resonance Image Reconstruction Using Federated Learning	Johns Hopkins University	CVPR	2021	FL-MRCM ²⁰⁷	[PUB.] [PUB.] PDF [Code]
Model-Contrastive Federated Learning 🔥	NUS; UC Berkeley	CVPR	2021	MOON ²⁰⁸	[PUB.] [PUB.] PDF [Code] [解读]
FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space 🔥	CUHK	CVPR	2021	FedDG-ELCFS ²⁰⁹	[PUB.] [PUB.] PDF [Code]
Soteria: Provable Defense Against Privacy Leakage in Federated Learning From Representation Perspective	Duke University	CVPR	2021	Soteria ²¹⁰	[PUB.] [PUB.] PDF [Code]
Federated Learning for Non-IID Data via Unified Feature Learning and Optimization Objective Alignment	PKU	ICCV	2021	FedUFO ²¹¹	[PUB.]
Ensemble Attention Distillation for Privacy-Preserving Federated Learning	University at Buffalo	ICCV	2021	FedAD ²¹²	[PUB.] [PDF]
Collaborative Unsupervised Visual Representation Learning from Decentralized Data	NTU; SenseTime	ICCV	2021	FedU ²¹³	[PUB.] PDF PDF
Joint Optimization in Edge-Cloud Continuum for Federated Unsupervised Person Re-identification	NTU	MM	2021	FedUReID ²¹⁴	[PUB.] [PDF]
Federated Visual Classification with Real-World Data Distribution	MIT; Google	ECCV	2020	FedVC+FedIR ²¹⁵	[PUB.] PDF Video
InvisibleFL: Federated Learning over Non-Informative Intermediate Updates against Multimedia Privacy Leakages		MM	2020	InvisibleFL ²¹⁶	[PUB.]
Performance Optimization of Federated Person Re-identification via Benchmark Analysis `data.`	NTU	MM	2020	FedReID ²¹⁷	[PUB.] [PDF] Code [解读]

FL in top NLP conference and journal

In this section, we will summarize Federated Learning papers accepted by top AI and NLP conference and journal, including ACL(Annual Meeting of the Association for Computational Linguistics), NAACL(North American Chapter of the Association for Computational Linguistics), EMNLP(Conference on Empirical Methods in Natural Language Processing) and COLING(International Conference on Computational Linguistics).

Title	Affiliation	Venue	Year	TL;DR	Materials
Scaling Language Model Size in Cross-Device Federated Learning	Google	ACL workshop	2022		[PUB.] PDF
Intrinsic Gradient Compression for Scalable and Efficient Federated Learning	Oxford	ACL workshop	2022		[PUB.] PDF
ActPerFL: Active Personalized Federated Learning	Amazon	ACL workshop	2022	ActPerFL²¹⁸	[PUB.] Page
FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Tasks 🔥	USC	NAACL	2022	FedNLP²¹⁹	[PUB.] PDF Code
Federated Learning with Noisy User Feedback	USC; Amazon	NAACL	2022		[PUB.] PDF
Training Mixed-Domain Translation Models via Federated Learning	Amazon	NAACL	2022		[PUB.] Page PDF
Pretrained Models for Multilingual Federated Learning	Johns Hopkins University	NAACL	2022		[PUB.] PDF Code
Training Mixed-Domain Translation Models via Federated Learning	Amazon	NAACL	2022		[PUB.] Page PDF
Federated Chinese Word Segmentation with Global Character Associations	University of Washington	ACL workshop	2021		[PUB.] code
Efficient-FedRec: Efficient Federated Learning Framework for Privacy-Preserving News Recommendation	USTC	EMNLP	2021	Efficient-FedRec²²⁰	[PUB.] PDF Code Video
Improving Federated Learning for Aspect-based Sentiment Analysis via Topic Memories	CUHK (Shenzhen)	EMNLP	2021		[PUB.] Code Video
A Secure and Efficient Federated Learning Framework for NLP	University of Connecticut	EMNLP	2021		[PUB.] PDF Video
Distantly Supervised Relation Extraction in Federated Settings	UCAS	EMNLP workshop	2021		[PUB.] PDF Code
Federated Learning with Noisy User Feedback	USC; Amazon	NAACL workshop	2021		[PUB.] PDF
An Investigation towards Differentially Private Sequence Tagging in a Federated Framework	Universität Hamburg	NAACL workshop	2021		[PUB.]
Understanding Unintended Memorization in Language Models Under Federated Learning	Google	NAACL workshop	2021		[PUB.] PDF
FedED: Federated Learning via Ensemble Distillation for Medical Relation Extraction	CAS	EMNLP	2020		[PUB.] Video [解读]
Empirical Studies of Institutional Federated Learning For Natural Language Processing	Ping An Technology	EMNLP workshop	2020		[PUB.]
Federated Learning for Spoken Language Understanding	PKU	COLING	2020		[PUB.]
Two-stage Federated Phenotyping and Patient Representation Learning	Boston Children’s Hospital Harvard Medical School	ACL workshop	2019		[PUB.] PDF Code UC.

FL in top IR conference and journal

In this section, we will summarize Federated Learning papers accepted by top Information Retrieval conference and journal, including SIGIR(Annual International ACM SIGIR Conference on Research and Development in Information Retrieval).

SIGIR 2022, 2021, 2020

Title	Affiliation	Venue	Year	TL;DR	Materials
Is Non-IID Data a Threat in Federated Online Learning to Rank?	The University of Queensland	SIGIR	2022	noniid-foltr²²¹	[PUB.] Code
FedCT: Federated Collaborative Transfer for Recommendation	Rutgers University	SIGIR	2021	FedCT²²²	[PUB.] PDF Code
On the Privacy of Federated Pipelines	Technical University of Munich	SIGIR	2021	FedGWAS²²³	[PUB.]
FedCMR: Federated Cross-Modal Retrieval.	Dalian University of Technology	SIGIR	2021	FedCMR²²⁴	[PUB.] Code
Meta Matrix Factorization for Federated Rating Predictions.	SDU	SIGIR	2020	MetaMF²²⁵	[PUB.] PDF

FL in top DB conference and journal

In this section, we will summarize Federated Learning papers accepted by top Database conference and journal, including SIGMOD(ACM SIGMOD Conference) , ICDE(IEEE International Conference on Data Engineering) and VLDB(Very Large Data Bases Conference).

Title	Affiliation	Venue	Year	TL;DR	Materials
Improving Fairness for Data Valuation in Horizontal Federated Learning	The UBC	ICDE	2022		[PUB.] PDF
FedADMM: A Robust Federated Deep Learning Framework with Adaptivity to System Heterogeneity	USTC	ICDE	2022	FedADMM²²⁶	[PUB.] PDF Code
FedMP: Federated Learning through Adaptive Model Pruning in Heterogeneous Edge Computing.	USTC	ICDE	2022	FedMP²²⁷	[PUB.]
Federated Learning on Non-IID Data Silos: An Experimental Study. 🔥	NUS	ICDE	2022		[PUB.] PDF Code
Enhancing Federated Learning with Intelligent Model Migration in Heterogeneous Edge Computing	USTC	ICDE	2022	FedMigr²²⁸	[PUB.]
Samba: A System for Secure Federated Multi-Armed Bandits		ICDE	2022	Samba²²⁹	[PUB.] Code
FedRecAttack: Model Poisoning Attack to Federated Recommendation	ZJU	ICDE	2022	FedRecAttack²³⁰	[PUB.] PDF Code
Enhancing Federated Learning with In-Cloud Unlabeled Data	USTC	ICDE	2022		[PUB.]
Efficient Participant Contribution Evaluation for Horizontal and Vertical Federated Learning	USTC	ICDE	2022	DIG-FL²³¹	[PUB.]
An Introduction to Federated Computation	University of Warwick; Facebook	SIGMOD	2022		[PUB.]
BlindFL: Vertical Federated Machine Learning without Peeking into Your Data	PKU; Tencent	SIGMOD	2022	BlindFL²³²	[PUB.] PDF
An Efficient Approach for Cross-Silo Federated Learning to Rank		ICDE	2021		[PUB.] [Related Paper(zh)]
Feature Inference Attack on Model Predictions in Vertical Federated Learning		ICDE	2021		[PUB.] PDF Code
Efficient Federated-Learning Model Debugging		ICDE	2021		[PUB.]
Federated Matrix Factorization with Privacy Guarantee	Purdue	VLDB	2021		[PUB.]
Projected Federated Averaging with Heterogeneous Differential Privacy.	Renmin University of China	VLDB	2021		[PUB.] Code
Enabling SQL-based Training Data Debugging for Federated Learning	Simon Fraser University	VLDB	2021	FedRain-and-Frog²³³	[PUB.] PDF Code
Refiner: A Reliable Incentive-Driven Federated Learning System Powered by Blockchain	ZJU	VLDB	2021		[PUB.]
Tanium Reveal: A Federated Search Engine for Querying Unstructured File Data on Large Enterprise Networks	Tanium Inc.	VLDB	2021		[PUB.] Video
VF2Boost: Very Fast Vertical Federated Gradient Boosting for Cross-Enterprise Learning	PKU	SIGMOD	2021	VF2Boost⁶⁰	[PUB.]
ExDRa: Exploratory Data Science on Federated Raw Data		SIGMOD	2021		[PUB.]
Joint blockchain and federated learning-based offloading in harsh edge computing environments		SIGMOD workshop	2021		[PUB.]
Privacy Preserving Vertical Federated Learning for Tree-based Models	NUS	VLDB	2020	Pivot-DT⁶⁵	[PUB.] PDF [Video] Code

FL in top Network conference and journal

In this section, we will summarize Federated Learning papers accepted by top Database conference and journal, including SIGCOMM(Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication), INFOCOM(IEEE Conference on Computer Communications), MobiCom(ACM/IEEE International Conference on Mobile Computing and Networking), NSDI(Symposium on Networked Systems Design and Implementation) and WWW(The Web Conference).

SIGCOMM NULL
INFOCOM 2022(Page), 2021(Page), 2020(Page), 2019, 2018
MobiCom 2021, 2020
NSDI NULL
WWW 2022, 2021

Title	Affiliation	Venue	Year	TL;DR	Materials
Joint Superposition Coding and Training for Federated Learning over Multi-Width Neural Networks	Korea University	INFOCOM	2022		[PUB.]
Towards Optimal Multi-Modal Federated Learning on Non-IID Data with Hierarchical Gradient Blending	University of Toronto	INFOCOM	2022		[PUB.]
Optimal Rate Adaption in Federated Learning with Compressed Communications	SZU	INFOCOM	2022		[PUB.] PDF
The Right to be Forgotten in Federated Learning: An Efficient Realization with Rapid Retraining.	CityU	INFOCOM	2022		[PUB.] PDF
Tackling System and Statistical Heterogeneity for Federated Learning with Adaptive Client Sampling.	CUHK; AIRS ;Yale University	INFOCOM	2022		[PUB.] PDF
Communication-Efficient Device Scheduling for Federated Learning Using Stochastic Optimization	Army Research Laboratory, Adelphi	INFOCOM	2022		[PUB.] PDF
FLASH: Federated Learning for Automated Selection of High-band mmWave Sectors	NEU	INFOCOM	2022	FLASH²³⁴	[PUB.] Code
A Profit-Maximizing Model Marketplace with Differentially Private Federated Learning	CUHK; AIRS	INFOCOM	2022		[PUB.]
Protect Privacy from Gradient Leakage Attack in Federated Learning	PolyU	INFOCOM	2022		[PUB.] Slides
FedFPM: A Unified Federated Analytics Framework for Collaborative Frequent Pattern Mining.	SJTU	INFOCOM	2022	FedFPM²³⁵	[PUB.] Code
An Accuracy-Lossless Perturbation Method for Defending Privacy Attacks in Federated Learning	SWJTU;THU	WWW	2022	PBPFL²³⁶	[PUB.] PDF Code
LocFedMix-SL: Localize, Federate, and Mix for Improved Scalability, Convergence, and Latency in Split Learning	Yonsei University	WWW	2022	LocFedMix-SL²³⁷	[PUB.]
Federated Unlearning via Class-Discriminative Pruning	PolyU	WWW	2022		[PUB.] PDF Code Code
FedKC: Federated Knowledge Composition for Multilingual Natural Language Understanding	Purdue	WWW	2022	FedKC²³⁸	[PUB.]
Federated Bandit: A Gossiping Approach	University of California	SIGMETRICS	2021	Federated Bandit ²³⁹	[PUB.] PDF
Hermes: an efficient federated learning framework for heterogeneous mobile clients	Duke University	MobiCom	2021	Hermes²⁴⁰	[PUB.]
Federated mobile sensing for activity recognition	Samsung AI Center	MobiCom	2021		[PUB.] Page Talks Video
Learning for Learning: Predictive Online Control of Federated Learning with Edge Provisioning.	Nanjing University	INFOCOM	2021		[PUB.]
Device Sampling for Heterogeneous Federated Learning: Theory, Algorithms, and Implementation.	Purdue	INFOCOM	2021	D2D-FedL¹⁸	[PUB.] PDF
FAIR: Quality-Aware Federated Learning with Precise User Incentive and Model Aggregation	THU	INFOCOM	2021	FAIR²⁴¹	[PUB.]
Sample-level Data Selection for Federated Learning	USTC	INFOCOM	2021		[PUB.]
To Talk or to Work: Flexible Communication Compression for Energy Efficient Federated Learning over Heterogeneous Mobile Edge Devices	Xidian University; CAS	INFOCOM	2021		[PUB.] PDF
Cost-Effective Federated Learning Design	CUHK; AIRS; Yale University	INFOCOM	2021		[PUB.] PDF
An Incentive Mechanism for Cross-Silo Federated Learning: A Public Goods Perspective	The UBC	INFOCOM	2021		[PUB.]
Resource-Efficient Federated Learning with Hierarchical Aggregation in Edge Computing	USTC	INFOCOM	2021		[PUB.]
FedServing: A Federated Prediction Serving Framework Based on Incentive Mechanism.	Jinan University; CityU	INFOCOM	2021	FedServing²⁴²	[PUB.] PDF
Federated Learning over Wireless Networks: A Band-limited Coordinated Descent Approach	Arizona State University	INFOCOM	2021		[PUB.] PDF
Dual Attention-Based Federated Learning for Wireless Traffic Prediction	King Abdullah University of Science and Technology	INFOCOM	2021	FedDA²⁴³	[PUB.] PDF Code Code
FedSens: A Federated Learning Approach for Smart Health Sensing with Class Imbalance in Resource Constrained Edge Computing	University of Notre Dame	INFOCOM	2021	FedSens²⁴⁴	[PUB.]
P-FedAvg: Parallelizing Federated Learning with Theoretical Guarantees	SYSU; Guangdong Key Laboratory of Big Data Analysis and Processing	INFOCOM	2021	P-FedAvg²⁴⁵	[PUB.]
Meta-HAR: Federated Representation Learning for Human Activity Recognition.	University of Alberta	WWW	2021	Meta-HAR²⁴⁶	[PUB.] PDF Code
PFA: Privacy-preserving Federated Adaptation for Effective Model Personalization	PKU	WWW	2021	PFA²⁴⁷	[PUB.] PDF Code
Communication Efficient Federated Generalized Tensor Factorization for Collaborative Health Data Analytics	Emory	WWW	2021	FedGTF-EF-PC²⁴⁸	[PUB.] Code
Hierarchical Personalized Federated Learning for User Modeling	USTC	WWW	2021		[PUB.]
Characterizing Impacts of Heterogeneity in Federated Learning upon Large-Scale Smartphone Data	PKU	WWW	2021		[PUB.] Slides Code
Incentive Mechanism for Horizontal Federated Learning Based on Reputation and Reverse Auction	SYSU	WWW	2021		[PUB.]
Physical-Layer Arithmetic for Federated Learning in Uplink MU-MIMO Enabled Wireless Networks.	Nanjing University	INFOCOM	2020		[PUB.]
Optimizing Federated Learning on Non-IID Data with Reinforcement Learning 🔥	University of Toronto	INFOCOM	2020		[PUB.] Slides Code [解读]
Enabling Execution Assurance of Federated Learning at Untrusted Participants	THU	INFOCOM	2020		[PUB.] Code
Billion-scale federated learning on mobile clients: a submodel design with tunable privacy	SJTU	MobiCom	2020		[PUB.]
Federated Learning over Wireless Networks: Optimization Model Design and Analysis	The University of Sydney	INFOCOM	2019		[PUB.] Code
Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning	Wuhan University	INFOCOM	2019		[PUB.] PDF UC.
InPrivate Digging: Enabling Tree-based Distributed Data Mining with Differential Privacy	Collaborative Innovation Center of Geospatial Technology	INFOCOM	2018	TFL²⁴⁹	[PUB.]

FL in top System conference and journal

In this section, we will summarize Federated Learning papers accepted by top Database conference and journal, including OSDI(USENIX Symposium on Operating Systems Design and Implementation), SOSP(Symposium on Operating Systems Principles), ISCA(International Symposium on Computer Architecture), MLSys(Conference on Machine Learning and Systems), TPDS(IEEE Transactions on Parallel and Distributed Systems).

OSDI 2021
SOSP 2021
ISCA NULL
MLSys 2022, 2020, 2019
TPDS 2022, 2021, 2020

Title	Affiliation	Venue	Year	TL;DR	Materials
FedGraph: Federated Graph Learning with Intelligent Sampling	UoA	TPDS	2022	FedGraph⁸	[PUB.] Code [解读]
AUCTION: Automated and Quality-Aware Client Selection Framework for Efficient Federated Learning.	THU	TPDS	2022	AUCTION²⁵⁰	[PUB.]
DONE: Distributed Approximate Newton-type Method for Federated Edge Learning.	University of Sydney	TPDS	2022	DONE²⁵¹	[PUB.] PDF Code
Flexible Clustered Federated Learning for Client-Level Data Distribution Shift.	CQU	TPDS	2022	FlexCFL²⁵²	[PUB.] PDF Code
Min-Max Cost Optimization for Efficient Hierarchical Federated Learning in Wireless Edge Networks.	Xidian University	TPDS	2022		[PUB.]
LightFed: An Efficient and Secure Federated Edge Learning System on Model Splitting.	CSU	TPDS	2022	LightFed²⁵³	[PUB.]
On the Benefits of Multiple Gossip Steps in Communication-Constrained Decentralized Federated Learning.	Purdue	TPDS	2022	Deli-CoCo²⁵⁴	[PUB.] PDF Code
Incentive-Aware Autonomous Client Participation in Federated Learning.	Sun Yat-sen University	TPDS	2022		[PUB.]
Communicational and Computational Efficient Federated Domain Adaptation.	HKUST	TPDS	2022		[PUB.]
Decentralized Edge Intelligence: A Dynamic Resource Allocation Framework for Hierarchical Federated Learning.	NTU	TPDS	2022		[PUB.]
Differentially Private Byzantine-Robust Federated Learning.	Qufu Normal University	TPDS	2022	DPBFL²⁵⁵	[PUB.]
Multi-Task Federated Learning for Personalised Deep Neural Networks in Edge Computing.	University of Exeter	TPDS	2022		[PUB.] PDF Code Code
Reputation-Aware Hedonic Coalition Formation for Efficient Serverless Hierarchical Federated Learning.	BUAA	TPDS	2022	SHFL²⁵⁶	[PUB.]
Differentially Private Federated Temporal Difference Learning.	Stony Brook University	TPDS	2022		[PUB.]
Towards Efficient and Stable K-Asynchronous Federated Learning With Unbounded Stale Gradients on Non-IID Data.	XJTU	TPDS	2022	WKAFL²⁵⁷	[PUB.] PDF
Communication-Efficient Federated Learning With Compensated Overlap-FedAvg.	SCU	TPDS	2022	Overlap-FedAvg²⁵⁸	[PUB.] PDF Code
PAPAYA: Practical, Private, and Scalable Federated Learning.	Meta AI	MLSys	2022	PAPAYA²⁵⁹	[PDF] [PUB.]
LightSecAgg: a Lightweight and Versatile Design for Secure Aggregation in Federated Learning	USC	MLSys	2022	LightSecAgg²⁶⁰	[PDF] [PUB.] Code
Oort: Efficient Federated Learning via Guided Participant Selection	University of Michigan	OSDI	2021	Oort²⁶¹	[PUB.] [PDF] Code Slides Video
Towards Efficient Scheduling of Federated Mobile Devices Under Computational and Statistical Heterogeneity.	Old Dominion University	TPDS	2021		[PUB.] PDF
Self-Balancing Federated Learning With Global Imbalanced Data in Mobile Systems.	CQU	TPDS	2021	Astraea²⁶²	[PUB.] Code
An Efficiency-Boosting Client Selection Scheme for Federated Learning With Fairness Guarantee	SCUT	TPDS	2021	RBCS-F²⁶³	[PUB.] [PDF] [解读]
Proof of Federated Learning: A Novel Energy-Recycling Consensus Algorithm.	Beijing Normal University	TPDS	2021	PoFL²⁶⁴	[PUB.] PDF
Biscotti: A Blockchain System for Private and Secure Federated Learning.	UBC	TPDS	2021	Biscotti²⁶⁵	[PUB.]
Mutual Information Driven Federated Learning.	Deakin University	TPDS	2021		[PUB.]
Accelerating Federated Learning Over Reliability-Agnostic Clients in Mobile Edge Computing Systems.	University of Warwick	TPDS	2021		[PUB.] PDF
FedSCR: Structure-Based Communication Reduction for Federated Learning.	HKU	TPDS	2021	FedSCR²⁶⁶	[PUB.]
FedScale: Benchmarking Model and System Performance of Federated Learning 🔥	University of Michigan	SOSP workshop / ICML 2022	2021	FedScale¹⁰⁹	[PUB.] [PUB.] PDF code [解读]
Redundancy in cost functions for Byzantine fault-tolerant federated learning		SOSP workshop	2021		[PUB.]
Towards an Efficient System for Differentially-private, Cross-device Federated Learning		SOSP workshop	2021		[PUB.]
GradSec: a TEE-based Scheme Against Federated Learning Inference Attacks		SOSP workshop	2021		[PUB.]
Community-Structured Decentralized Learning for Resilient EI.		SOSP workshop	2021		[PUB.]
Separation of Powers in Federated Learning (Poster Paper)	IBM Research	SOSP workshop	2021	TRUDA²⁶⁷	[PUB.] PDF
Accelerating Federated Learning via Momentum Gradient Descent.	USTC	TPDS	2020	MFL²⁶⁸	[PUB.] PDF
Towards Fair and Privacy-Preserving Federated Deep Models.	NUS	TPDS	2020	FPPDL²⁶⁹	[PUB.] PDF Code
Federated Optimization in Heterogeneous Networks 🔥	CMU	MLSys	2020	FedProx²⁷⁰	[PUB.] [PDF] Code
Towards Federated Learning at Scale: System Design	Google	MLSys	2019	System_Design ²⁷¹	[PUB.] [PDF] [解读]

Framework

Federated Learning Framework

Table

Note: SG means Support for Graph data and algorithms, ST means Support for Tabular data and algorithms.

Platform	Papers	Affiliations	SG	ST	Materials
PySyft	A generic framework for privacy preserving deep learning	OpenMined			Doc
FATE	FATE: An Industrial Grade Platform for Collaborative Learning With Data Protection	WeBank		✅✅	Doc Doc(zh)
MindSpore Federated		HUAWEI			Doc Page
TFF(Tensorflow-Federated)	Towards Federated Learning at Scale: System Design	Google			Doc Page
FedML	FedML: A Research Library and Benchmark for Federated Machine Learning	FedML	✅✅	✅	Doc
Flower	Flower: A Friendly Federated Learning Research Framework	flower.dev adap			Doc
Fedlearner		Bytedance
SecretFlow		Ant group		✅	Doc
FederatedScope	FederatedScope: A Flexible Federated Learning Platform for Heterogeneity	Alibaba DAMO Academy	✅✅		Doc Page
LEAF	LEAF: A Benchmark for Federated Settings	CMU
Rosetta		matrixelements			Doc Page
PaddleFL		Baidu			Doc
OpenFL	OpenFL: An open-source framework for Federated Learning	Intel			Doc
IBM Federated Learning	IBM Federated Learning: an Enterprise Framework White Paper	IBM		✅	Papers
KubeFATE		WeBank			Wiki
Privacy Meter	Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning	University of Massachusetts Amherst
Fedlab	FedLab: A Flexible Federated Learning Framework	SMILELab			Doc Doc(zh) Page
Differentially Private Federated Learning: A Client-level Perspective	Differentially Private Federated Learning: A Client Level Perspective	SAP-samples
NVFlare		NVIDIA			Doc
easyFL	Federated Learning with Fair Averaging	XMU
FedScale	FedScale: Benchmarking Model and System Performance of Federated Learning at Scale	SymbioticLab(U-M)
FedNLP	FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Tasks	FedML
Backdoors 101	Blind Backdoors in Deep Learning Models	Cornell Tech
FedJAX	FEDJAX: Federated learning simulation with JAX	Google
PFL-Non-IID		SJTU
Xaynet		XayNet			Page Doc Whitepaper Legal Review
SyferText		OpenMined
Galaxy Federated Learning	GFL: A Decentralized Federated Learning Framework Based On Blockchain	ZJU			Doc
FedGraphNN	FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks	FedML	✅✅
plato		UofT
NIID-Bench	Federated Learning on Non-IID Data Silos: An Experimental Study	Xtra Computing Group
SWARM LEARNING	Swarm Learning for decentralized and confidential clinical machine learning				Video
substra		Substra			Doc
PhotoLabeller					Blog
FLSim		facebook research
Primihub		primihub			Doc
PyVertical	PyVertical: A Vertical Federated Learning Framework for Multi-headed SplitNN	OpenMined
FedTorch	Distributionally Robust Federated Averaging	Penn State
FATE-Serving		WeBank			Doc
PriMIA	End-to-end privacy preserving deep learning on multi-institutional medical imaging	TUM; Imperial College London; OpenMined			Doc
FLUTE	FLUTE: A Scalable, Extensible Framework for High-Performance Federated Learning Simulations	microsoft			Doc
FLSim	Optimizing Federated Learning on Non-IID Data with Reinforcement Learning	University of Toronto
Breaching	A Framework for Attacks against Privacy in Federated Learning (papers)
9nfl		JD
EasyFL	EasyFL: A Low-code Federated Learning Platform For Dummies	NTU
FedLearn	Fedlearn-Algo: A flexible open-source privacy-preserving machine learning platform	JD
FEDn	Scalable federated machine learning with FEDn	scaleoutsystems			Doc
FedCV	FedCV: A Federated Learning Framework for Diverse Computer Vision Tasks	FedML
FedTree		Xtra Computing Group		✅✅	Doc
MPLC		LabeliaLabs			Page
OpenHealth		ZJU
UCADI	Advancing COVID-19 diagnosis with privacy-preserving collaboration in artificial intelligence	Huazhong University of Science and Technology
OpenFed	OpenFed: A Comprehensive and Versatile Open-Source Federated Learning Framework				Doc
FlexCFL	Flexible Clustered Federated Learning for Client-Level Data Distribution Shift	Chongqing University
FedGroup	FedGroup: Efficient Clustered Federated Learning via Decomposed Data-Driven Measure	Chongqing University
FedEval	FedEval: A Benchmark System with a Comprehensive Evaluation Model for Federated Learning	HKU			Doc
APPFL					Doc
Flame		Cisco
Federated-Learning-source	A Practical Federated Learning Framework for Small Number of Stakeholders	ETH Zürich			Doc
Clara		NVIDIA

Benchmark

UniFed leaderboard

Here's a really great Benchmark for the federated learning open source framework 👍 UniFed leaderboard, which present both qualitative and quantitative evaluation results of existing popular open-sourced FL frameworks, from the perspectives of functionality, usability, and system performance.

For more results, please refer to Framework Functionality Support

Datasets

Tutorials

Federated Learning on MNIST using a CNN, AI6101, 2020 (Demo Video)
Federated Learning: User Privacy, Data Security and Confidentiality in Machine Learning, AAAI-19, Honolulu, HI, USA

Key Conferences/Workshops/Journals

This section partially refers to The Federated Learning Portal.

Workshops

[AI Technology School 2022] Trustable, Verifiable and Auditable Artificial Intelligence, Singapore
[FL-NeurIPS'22] International Workshop on Federated Learning: Recent Advances and New Challenges in Conjunction with NeurIPS 2022 , New Orleans, LA, USA
[FL-IJCAI'22] International Workshop on Trustworthy Federated Learning in Conjunction with IJCAI 2022, Vienna, Austria
[FL-AAAI-22] International Workshop on Trustable, Verifiable and Auditable Federated Learning in Conjunction with AAAI 2022, Vancouver, BC, Canada (Virtual)
[FL-NeurIPS'21] New Frontiers in Federated Learning: Privacy, Fairness, Robustness, Personalization and Data Ownership, (Virtual)
[The Federated Learning Workshop, 2021] , Paris, France (Hybrid)
[PDFL-EMNLP'21] Workshop on Parallel, Distributed, and Federated Learning, Bilbao, Spain (Virtual)
[FTL-IJCAI'21] International Workshop on Federated and Transfer Learning for Data Sparsity and Confidentiality in Conjunction with IJCAI 2021, Montreal, QB, Canada (Virtual)
[DeepIPR-IJCAI'21] Toward Intellectual Property Protection on Deep Learning as a Services, Montreal, QB, Canada (Virtual)
[FL-ICML'21] International Workshop on Federated Learning for User Privacy and Data Confidentiality, (Virtual)
[RSEML-AAAI-21] Towards Robust, Secure and Efficient Machine Learning, (Virtual)
[NeurIPS-SpicyFL'20] Workshop on Scalability, Privacy, and Security in Federated Learning, Vancouver, BC, Canada (Virtual)
[FL-IJCAI'20] International Workshop on Federated Learning for User Privacy and Data Confidentiality, Yokohama, Japan (Virtual)
[FL-ICML'20] International Workshop on Federated Learning for User Privacy and Data Confidentiality, Vienna, Austria (Virtual)
[FL-IBM'20] Workshop on Federated Learning and Analytics, New York, NY, USA
[FL-NeurIPS'19] Workshop on Federated Learning for Data Privacy and Confidentiality (in Conjunction with NeurIPS 2019), Vancouver, BC, Canada
[FL-IJCAI'19] International Workshop on Federated Learning for User Privacy and Data Confidentiality in Conjunction with IJCAI 2019, Macau
[FL-Google'19] Workshop on Federated Learning and Analytics, Seattle, WA, USA

Journal Special Issues

Special Issue on Trustable, Verifiable, and Auditable Federated Learning, IEEE Transactions on Big Data (TBD), 2022.
Special Issue on Federated Learning: Algorithms, Systems, and Applications, ACM Transactions on Intelligent Systems and Technology (TIST), 2021.
Special Issue on Federated Machine Learning, IEEE Intelligent Systems (IS), 2019.

Conference Special Tracks

"Federated Learning" included as a new keyword in IJCAI'20, Yokohama, Japan
Special Track on Federated Machine Learning, IEEE BigData'19, Los Angeles, CA, USA

How to contact us

More items will be added to the repository. Please feel free to suggest other key resources by opening an issue report, submitting a pull request, or dropping me an email @ (im.young@foxmail.com). Enjoy reading!

Acknowledgments

Many thanks ❤️ to the other awesome list:

Federated Learning
Other fields
- anomaly-detection-resources
- awesome-image-registration

[^FCFL ]:

FedWalk, a random-walk-based unsupervised node embedding algorithm that operates in such a node-level visibility graph with raw graph information remaining locally. FedWalk，一个基于随机行走的无监督节点嵌入算法，在这样一个节点级可见度图中操作，原始图信息保留在本地。 ↩ ↩²
FederatedScope-GNN present an easy-to-use FGL (federated graph learning) package. FederatedScope-GNN提出了一个易于使用的FGL（联邦图学习）软件包。 ↩ ↩²
GAMF formulate the model fusion problem as a graph matching task, considering the second-order similarity of model weights instead of previous work merely formulating model fusion as a linear assignment problem. For the rising problem scale and multi-model consistency issues, GAMF propose an efficient graduated assignment-based model fusion method, iteratively updates the matchings in a consistency-maintaining manner. GAMF将模型融合问题表述为图形匹配任务，考虑了模型权重的二阶相似性，而不是之前的工作仅仅将模型融合表述为一个线性赋值问题。针对问题规模的扩大和多模型的一致性问题，GAMF提出了一种高效的基于分级赋值的模型融合方法，以保持一致性的方式迭代更新匹配结果。 ↩
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FedPerGNN, a federated GNN framework for both effective and privacy-preserving personalization. Through a privacy-preserving model update method, we can collaboratively train GNN models based on decentralized graphs inferred from local data. To further exploit graph information beyond local interactions, we introduce a privacy-preserving graph expansion protocol to incorporate high-order information under privacy protection. FedPerGNN是一个既有效又保护隐私的GNN联盟框架。通过一个保护隐私的模型更新方法，我们可以根据从本地数据推断出的分散图来协作训练GNN模型。为了进一步利用本地互动以外的图信息，我们引入了一个保护隐私的图扩展协议，在保护隐私的前提下纳入高阶信息。 ↩ ↩²
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We explore the threat of collusion attacks from multiple malicious clients who pose targeted attacks (e.g., label flipping) in a federated learning configuration. By leveraging client weights and the correlation among them, we develop a graph-based algorithm to detect malicious clients. 我们探讨了来自多个恶意客户的串通攻击的威胁，这些客户在联合学习配置中提出了有针对性的攻击（例如，标签翻转）。通过利用客户端的权重和它们之间的关联性，我们开发了一种基于图的算法来检测恶意客户端。 ↩ ↩²
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Cross-Node Federated Graph Neural Network (CNFGNN) , a federated spatio-temporal model, which explicitly encodes the underlying graph structure using graph neural network (GNN)-based architecture under the constraint of cross-node federated learning, which requires that data in a network of nodes is generated locally on each node and remains decentralized. CNFGNN operates by disentangling the temporal dynamics modeling on devices and spatial dynamics on the server, utilizing alternating optimization to reduce the communication cost, facilitating computations on the edge devices. 跨节点联邦图神经网络（CNFGNN），是一个联邦时空模型，在跨节点联邦学习的约束下，使用基于图神经网络（GNN）的架构对底层图结构进行显式编码，这要求节点网络中的数据是在每个节点上本地生成的，并保持分散。CNFGNN通过分解设备上的时间动态建模和服务器上的空间动态来运作，利用交替优化来降低通信成本，促进边缘设备的计算。 ↩ ↩²
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FGML a comprehensive review of the literature in Federated Graph Machine Learning. FGML 对图联邦机器学习的文献进行了全面回顾的综述文章。 ↩
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Federated functional gradient boosting (FFGB). Under appropriate assumptions on the weak learning oracle, the FFGB algorithm is proved to efficiently converge to certain neighborhoods of the global optimum. The radii of these neighborhoods depend upon the level of heterogeneity measured via the total variation distance and the much tighter Wasserstein-1 distance, and diminish to zero as the setting becomes more homogeneous. ↩
Federated Random Forests (FRF) models, focusing particularly on the heterogeneity within and between datasets. 联邦随机森林（FRF）模型，特别关注数据集内部和之间的异质性。 ↩
Federated Forest , which is a lossless learning model of the traditional random forest method, i.e., achieving the same level of accuracy as the non-privacy-preserving approach. Based on it, we developed a secure cross-regional machine learning system that allows a learning process to be jointly trained over different regions’ clients with the same user samples but different attribute sets, processing the data stored in each of them without exchanging their raw data. A novel prediction algorithm was also proposed which could largely reduce the communication overhead. Federated Forest ，是传统随机森林方法的无损学习模型，即达到与非隐私保护方法相同的准确度。在此基础上，我们开发了一个安全的跨区域机器学习系统，允许在具有相同用户样本但不同属性集的不同区域的客户端上联邦训练一个学习过程，处理存储在每个客户端的数据，而不交换其原始数据。还提出了一种新的预测算法，可以在很大程度上减少通信开销。 ↩
Fed-GBM (Federated Gradient Boosting Machines), a cost-effective collaborative learning framework, consisting of two-stage voting and node-level parallelism, to address the problems in co-modelling for Non-intrusive load monitoring (NILM). Fed-GBM(联邦梯度提升)是一个具有成本效益的协作学习框架，由两阶段投票和节点级并行组成，用于解决非侵入式负载监测(NILM)中的协同建模问题。 ↩
Efficient FL for GBDT (eFL-Boost), which minimizes accuracy loss, communication costs, and information leakage. The proposed scheme focuses on appropriate allocation of local computation (performed individually by each organization) and global computation (performed cooperatively by all organizations) when updating a model. A tree structure is determined locally at one of the organizations, and leaf weights are calculated globally by aggregating the local gradients of all organizations. Specifically, eFL-Boost requires only three communications per update, and only statistical information that has low privacy risk is leaked to other organizations. 针对GBDT的高效FL（eFL-Boost），将精度损失、通信成本和信息泄露降到最低。该方案的重点是在更新模型时适当分配局部计算（由每个组织单独执行）和全局计算（由所有组织合作执行）。树状结构由其中一个组织在本地确定，而叶子的权重则由所有组织的本地梯度汇总后在全局计算。具体来说，eFL-Boost每次更新只需要三次通信，而且只有具有低隐私风险的统计信息才会泄露给其他组织。 ↩
Random Forest Based on Federated Learning for Intrusion Detection 使用联邦随机森林做入侵检测 ↩
A federated decision tree-based random forest algorithm where a small number of organizations or industry companies collaboratively build models. 一个基于联邦决策树的随机森林算法，由少数组织或行业公司合作建立模型。 ↩
VF2Boost, a novel and efficient vertical federated GBDT system. First, to handle the deficiency caused by frequent mutual-waiting in federated training, we propose a concurrent training protocol to reduce the idle periods. Second, to speed up the cryptography operations, we analyze the characteristics of the algorithm and propose customized operations. Empirical results show that our system can be 12.8-18.9 times faster than the existing vertical federated implementations and support much larger datasets. VF2Boost，一个新颖而高效的纵向联邦GBDT系统。首先，为了处理联邦训练中频繁的相互等待造成的缺陷，我们提出了一个并发训练协议来减少空闲期。第二，为了加快密码学操作，我们分析了算法的特点，并提出了定制的操作。经验结果表明，我们的系统可以比现有的垂直联合实现快12.8-18.9倍，并支持更大的数据集。我们将保证公平性的客户选择建模为一个Lyapunov优化问题，然后提出一个基于C2MAB的方法来估计每个客户和服务器之间的模型交换时间，在此基础上，我们设计了一个保证公平性的算法，即RBCS-F来解决问题。 ↩ ↩²
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The proposed FL-XGBoost can train a sensitive task to be solved among different entities without revealing their own data. The proposed FL-XGBoost can achieve significant reduction in the number of communications between entities by exchanging decision tree models. FL-XGBoost可以训练一个敏感的任务，在不同的实体之间解决，而不透露他们自己的数据。所提出的FL-XGBoost可以通过交换决策树模型实现实体之间通信数量的大幅减少。 ↩
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A hybrid federated learning framework based on XGBoost, for distributed power prediction from real-time external features. In addition to introducing boosted trees to improve accuracy and interpretability, we combine horizontal and vertical federated learning, to address the scenario where features are scattered in local heterogeneous parties and samples are scattered in various local districts. Moreover, we design a dynamic task allocation scheme such that each party gets a fair share of information, and the computing power of each party can be fully leveraged to boost training efficiency. 一个基于XGBoost的混合联邦学习框架，用于从实时外部特征进行分布式电力预测。除了引入提升树来提高准确性和可解释性，我们还结合了横向和纵向的联邦学习，以解决特征分散在本地异质方和样本分散在不同本地区的情况。此外，我们设计了一个动态的任务分配方案，使每一方都能获得公平的信息份额，并能充分利用每一方的计算能力来提高训练效率。 ↩
Efficient XGBoost vertical federated learning. we proposed a novel batch homomorphic encryption method to cut the cost of encryption-related computation and transmission in nearly half. This is achieved by encoding the first-order derivative and the second-order derivative into a single number for encryption, ciphertext transmission, and homomorphic addition operations. The sum of multiple first-order derivatives and second-order derivatives can be simultaneously decoded from the sum of encoded values. 高效的XGBoost纵向联邦学习。我们提出了一种新颖的批量同态加密方法，将加密相关的计算和传输成本减少了近一半。这是通过将一阶导数和二阶导数编码为一个数字来实现的，用于加密、密码文本传输和同态加法操作。多个一阶导数和二阶导数的总和可以同时从编码值的总和中解密。 ↩
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Two variants of federated XGBoost with privacy guarantee: FedXGBoost-SMM and FedXGBoost-LDP. Our first protocol FedXGBoost-SMM deploys enhanced secure matrix multiplication method to preserve privacy with lossless accuracy and lower overhead than encryption-based techniques. Developed independently, the second protocol FedXGBoost-LDP is heuristically designed with noise perturbation for local differential privacy. 两种具有隐私保护的联邦XGBoost的变体：FedXGBoost-SMM和FedXGBoost-LDP。FedXGBoost-SMM部署了增强的安全矩阵乘法，以无损的精度和低于基于加密的技术的开销来保护隐私。第二个协议FedXGBoost-LDP以启发式方法设计的，带有噪声扰动，用于保护局部差分隐私。 ↩
MP-FedXGB, a lossless multi-party federated XGB learning framework is proposed with a security guarantee, which reshapes the XGBoost's split criterion calculation process under a secret sharing setting and solves the leaf weight calculation problem by leveraging distributed optimization. MP-FedXGB是一个无损的多方联邦XGB学习框架，它在秘密共享的环境下重塑了XGBoost的分割准则计算过程，并通过利用分布式优化解决了叶子权重计算问题。 ↩
FederBoost for private federated learning of gradient boosting decision trees (GBDT). It supports running GBDT over both horizontally and vertically partitioned data. The key observation for designing FederBoost is that the whole training process of GBDT relies on the order of the data instead of the values. Consequently, vertical FederBoost does not require any cryptographic operation and horizontal FederBoost only requires lightweight secure aggregation. FederBoost用于梯度提升决策树（GBDT）的私有联合学习。它支持在横向和纵向分区的数据上运行GBDT。设计FederBoost的关键是，GBDT的整个训练过程依赖于数据的顺序而不是数值。因此，纵向FederBoost不需要任何加密操作，横向FederBoost只需要轻量级的安全聚合。 ↩
A horizontal federated XGBoost algorithm to solve the federated anomaly detection problem, where the anomaly detection aims to identify abnormalities from extremely unbalanced datasets and can be considered as a special classification problem. Our proposed federated XGBoost algorithm incorporates data aggregation and sparse federated update processes to balance the tradeoff between privacy and learning performance. In particular, we introduce the virtual data sample by aggregating a group of users' data together at a single distributed node. 一个横向联邦XGBoost算法来解决联合异常检测问题，其中异常检测的目的是从极不平衡的数据集中识别异常，可以被视为一个特殊的分类问题。我们提出的联合XGBoost算法包含了数据聚合和稀疏的联合更新过程，以平衡隐私和学习性能之间的权衡。特别是，我们通过将一组用户的数据聚集在一个分布式节点上，引入虚拟数据样本。 ↩
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FedScale, a federated learning (FL) benchmarking suite with realistic datasets and a scalable runtime to enable reproducible FL research. FedScale是一个联邦学习（FL）基准测试套件，具有现实的数据集和可扩展的运行时间，以实现可重复的FL研究。 ↩ ↩²
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CE propose the concept of benefit graph which describes how each client can benefit from collaborating with other clients and advance a Pareto optimization approach to identify the optimal collaborators. CE提出了利益图的概念，描述了每个客户如何从与其他客户的合作中获益，并提出了帕累托优化方法来确定最佳合作者。 ↩
SuPerFed, a personalized federated learning method that induces an explicit connection between the optima of the local and the federated model in weight space for boosting each other. SuPerFed，一种个性化联邦学习方法，该方法在本地模型和联邦模型的权重空间中诱导出一个明确的连接，以促进彼此的发展。 ↩
FedMSplit framework, which allows federated training over multimodal distributed data without assuming similar active sensors in all clients. The key idea is to employ a dynamic and multi-view graph structure to adaptively capture the correlations amongst multimodal client models. FedMSplit框架，该框架允许在多模态分布式数据上进行联邦训练，而不需要假设所有客户端都有类似的主动传感器。其关键思想是采用动态和多视图图结构来适应性地捕捉多模态客户模型之间的相关性。 ↩
Comm-FedBiO propose a learning-based reweighting approach to mitigate the effect of noisy labels in FL. Comm-FedBiO提出了一种基于学习的重加权方法，以减轻FL中噪声标签的影响。 ↩
FLDetector detects malicious clients via checking their model-updates consistency to defend against model poisoning attacks with a large number of malicious clients. FLDetector 通过检查其模型更新的一致性来检测恶意客户，以防御大量恶意客户的模型中毒攻击。 ↩
FedSVD, a practical lossless federated SVD method over billion-scale data, which can simultaneously achieve lossless accuracy and high efficiency. FedSVD，是一种实用的亿级数据上的无损联邦SVD方法，可以同时实现无损精度和高效率。 ↩
Federated Learning-to-Dispatch (Fed-LTD), a framework that allows effective order dispatching by sharing both dispatching models and decisions while providing privacy protection of raw data and high efficiency. 解决跨平台叫车问题，即多平台在不共享数据的情况下协同进行订单分配。 ↩
Felicitas is a distributed cross-device Federated Learning (FL) framework to solve the industrial difficulties of FL in large-scale device deployment scenarios. Felicitas是一个分布式的跨设备联邦学习（FL）框架，以解决FL在大规模设备部署场景中的工业困难。 ↩
InclusiveFL is to assign models of different sizes to clients with different computing capabilities, bigger models for powerful clients and smaller ones for weak clients. InclusiveFL 将不同大小的模型分配给具有不同计算能力的客户，较大的模型用于强大的客户，较小的用于弱小的客户。 ↩
FedAttack a simple yet effective and covert poisoning attack method on federated recommendation, core idea is using globally hardest samples to subvert model training. FedAttack是一种对联邦推荐的简单而有效的隐蔽中毒攻击方法，核心思想是利用全局最难的样本来颠覆模型训练。 ↩
PipAttack present a systematic approach to backdooring federated recommender systems for targeted item promotion. The core tactic is to take advantage of the inherent popularity bias that commonly exists in data-driven recommenders. PipAttack 提出了一种系统化的方法，为联邦推荐系统提供后门，以实现目标项目的推广。其核心策略是利用数据驱动的推荐器中普遍存在的固有的流行偏见。 ↩
Fed2, a feature-aligned federated learning framework to resolve this issue by establishing a firm structure-feature alignment across the collaborative models. Fed2是一个特征对齐的联邦学习框架，通过在协作模型之间建立牢固的结构-特征对齐来解决这个问题。 ↩
FedRS focus on a special kind of non-iid scene, i.e., label distribution skew, where each client can only access a partial set of the whole class set. Considering top layers of neural networks are more task-specific, we advocate that the last classification layer is more vulnerable to the shift of label distribution. Hence, we in-depth study the classifier layer and point out that the standard softmax will encounter several problems caused by missing classes. As an alternative, we propose “Restricted Softmax" to limit the update of missing classes’ weights during the local procedure. FedRS专注于一种特殊的非iid场景，即标签分布倾斜，每个客户端只能访问整个类集的部分集合。考虑到神经网络的顶层更具有任务针对性，我们主张最后一个分类层更容易受到标签分布偏移的影响。因此，我们深入研究了分类器层，并指出标准的softmax会遇到由缺失类引起的一些问题。作为一个替代方案，提出了 "限制性Softmax"，以限制在本地程序中对缺失类的权重进行更新。 ↩
While adversarial learning is commonly used in centralized learning for mitigating bias, there are significant barriers when extending it to the federated framework. In this work, we study these barriers and address them by proposing a novel approach Federated Adversarial DEbiasing (FADE). FADE does not require users' sensitive group information for debiasing and offers users the freedom to opt-out from the adversarial component when privacy or computational costs become a concern. 虽然对抗性学习通常用于集中式学习以减轻偏见，但当把它扩展到联邦式框架中时，会有很大的障碍。在这项工作中，我们研究了这些障碍，并通过提出一种新的方法 Federated Adversarial DEbiasing（FADE）来解决它们。FADE不需要用户的敏感群体信息来进行去偏，并且当隐私或计算成本成为一个问题时，用户可以自由地选择退出对抗性部分。 ↩
To address the challenges of communication and computation resource utilization, we propose an asynchronous stochastic quasi-Newton (AsySQN) framework for Vertical federated learning(VFL), under which three algorithms, i.e. AsySQN-SGD, -SVRG and -SAGA, are proposed. The proposed AsySQN-type algorithms making descent steps scaled by approximate (without calculating the inverse Hessian matrix explicitly) Hessian information convergence much faster than SGD-based methods in practice and thus can dramatically reduce the number of communication rounds. Moreover, the adopted asynchronous computation can make better use of the computation resource. We theoretically prove the convergence rates of our proposed algorithms for strongly convex problems. 为了解决通信和计算资源利用的挑战，我们提出了一个异步随机准牛顿（AsySQN）的纵和联邦学习VFL框架，在这个框架下，我们提出了三种算法，即AsySQN-SGD、-SVRG和-SAGA。所提出的AsySQN型算法使下降步骤按近似（不明确计算逆Hessian矩阵）Hessian信息收敛的速度比基于SGD的方法在实践中快得多，因此可以极大地减少通信轮数。此外，采用异步计算可以更好地利用计算资源。我们从理论上证明了我们提出的算法在强凸问题上的收敛率。 ↩
A simple yet effective algorithm, named Federated Learning on Medical Datasets using Partial Networks (FLOP), that shares only a partial model between the server and clients. 一种简单而有效的算法，被命名为使用部分网络的医学数据集的联邦学习（FLOP），该算法在服务器和客户之间只共享部分模型。 ↩
This paper have built a framework that enables Federated Learning (FL) for a small number of stakeholders. and described the framework architecture, communication protocol, and algorithms. 本文建立了一个框架，为少数利益相关者实现联邦学习（FL），并描述了框架架构、通信协议和算法。 ↩
A novel Federated Deep Knowledge Tracing (FDKT) framework to collectively train high-quality Deep Knowledge Tracing (DKT) models for multiple silos. 一个新颖的联邦深度知识追踪（FDKT）框架，为多个筒仓集体训练高质量的深度知识追踪（DKT）模型。 ↩
FedFast accelerates distributed learning which achieves good accuracy for all users very early in the training process. We achieve this by sampling from a diverse set of participating clients in each training round and applying an active aggregation method that propagates the updated model to the other clients. Consequently, with FedFast the users benefit from far lower communication costs and more accurate models that can be consumed anytime during the training process even at the very early stages. FedFast加速了分布式学习，在训练过程的早期为所有用户实现了良好的准确性。我们通过在每轮训练中从不同的参与客户中取样，并应用主动聚合方法，将更新的模型传播给其他客户来实现这一目标。因此，有了FedFast，用户可以从更低的通信成本和更准确的模型中受益，这些模型可以在训练过程中随时使用，即使是在最早期阶段。 ↩
FDSKL, a federated doubly stochastic kernel learning algorithm for vertically partitioned data. Specifically, we use random features to approximate the kernel mapping function and use doubly stochastic gradients to update the solutions, which are all computed federatedly without the disclosure of data. FDSKL，一个针对纵向分割数据的联邦双随机核学习算法。具体来说，我们使用随机特征来近似核映射函数，并使用双重随机梯度来更新解决方案，这些都是在不透露数据的情况下联邦计算的。 ↩
Federated Online Learning to Rank setup (FOLtR) where on-mobile ranking models are trained in a way that respects the users' privacy. FOLtR-ES that satisfies these requirement: (a) preserving the user privacy, (b) low communication and computation costs, (c) learning from noisy bandit feedback, and (d) learning with non-continuous ranking quality measures. A part of FOLtR-ES is a privatization procedure that allows it to provide ε-local differential privacy guarantees, i.e. protecting the clients from an adversary who has access to the communicated messages. This procedure can be applied to any absolute online metric that takes finitely many values or can be discretized to a finite domain. 联邦在线学习排名设置（FOLtR）中，移动端排名模型是以尊重用户隐私的方式来训练的。FOLtR-ES满足这些要求：(a)保护用户隐私，(b)低通信和计算成本，(c)从嘈杂的强盗反馈中学习，以及(d)用非连续的排名质量指标学习。FOLtR-ES的一部分是一个私有化程序，使其能够提供ε-local差异化的隐私保证，即保护客户不受能够接触到通信信息的对手的伤害。这个程序可以应用于任何绝对在线度量，其取值有限，或者可以离散到一个有限域。 ↩
We are motivated to resolve the above issue by proposing a solution, referred to as PEA (Private, Efficient, Accurate), which consists of a secure differentially private stochastic gradient descent (DPSGD for short) protocol and two optimization methods. First, we propose a secure DPSGD protocol to enforce DPSGD, which is a popular differentially private machine learning algorithm, in secret sharing-based MPL frameworks. Second, to reduce the accuracy loss led by differential privacy noise and the huge communication overhead of MPL, we propose two optimization methods for the training process of MPL. 提出一个安全差分隐私随机梯度下降协议以在基于秘密共享的安全多方学习框架中实现差分隐私随机梯度下降算法。为了降低差分隐私带来的精度损失并提升安全多方学习的效率，从安全多方学习训练过程的角度提出了两项优化方法，多方可以在MPL模型训练过程中平衡。做到隐私、效率和准确性三者之间的权衡。 ↩
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LC-Fed propose a personalized federated framework with Local Calibration, to leverage the inter-site in-consistencies in both feature- and prediction- levels to boost the segmentation. LC-Fed提出了一个带有本地校准的个性化联邦学习框架，以利用特征和预测层面的站点间不一致来提高分割效果。 ↩
Models trained in federated settings often suffer from degraded performances and fail at generalizing, especially when facing heterogeneous scenarios. FedSAM investigate such behavior through the lens of geometry of the loss and Hessian eigenspectrum, linking the model's lack of generalization capacity to the sharpness of the solution. 联邦学习环境下训练的模型经常会出现性能下降和泛化失败的情况，特别是在面对异质场景时。FedSAM 通过损失和Hessian特征谱的几何角度来研究这种行为，将模型缺乏泛化能力与解决方案的锐度联系起来。 ↩
ATPFL helps users federate multi-source trajectory datasets to automatically design and train a powerful TP model. ATPFL帮助用户联邦多源轨迹数据集，自动设计和训练强大的TP轨迹预测模型。 ↩
ViT-FL demonstrate that self-attention-based architectures (e.g., Transformers) are more robust to distribution shifts and hence improve federated learning over heterogeneous data. ViT-FL证明了基于自注意力机制架构（如 Transformers）对分布的转变更加稳健，从而改善了异构数据的联邦学习。 ↩
FedCorr, a general multi-stage framework to tackle heterogeneous label noise in FL, without making any assumptions on the noise models of local clients, while still maintaining client data privacy. FedCorr 一个通用的多阶段框架来处理FL中的异质标签噪声，不对本地客户的噪声模型做任何假设，同时仍然保持客户数据的隐私。 ↩
FedCor, an FL framework built on a correlation-based client selection strategy, to boost the convergence rate of FL. FedCor 一个建立在基于相关性的客户选择策略上的FL框架，以提高FL的收敛率。 ↩
A novel pFL training framework dubbed Layer-wised Personalized Federated learning (pFedLA) that can discern the importance of each layer from different clients, and thus is able to optimize the personalized model aggregation for clients with heterogeneous data. "层级个性化联邦学习"（pFedLA），它可以从不同的客户那里分辨出每一层的重要性，从而能够为拥有异质数据的客户优化个性化的模型聚合。 ↩
FedAlign rethinks solutions to data heterogeneity in FL with a focus on local learning generality rather than proximal restriction. 我们重新思考FL中数据异质性的解决方案，重点是本地学习的通用性(generality)而不是近似限制。 ↩
Position-Aware Neurons (PANs) , fusing position-related values (i.e., position encodings) into neuron outputs, making parameters across clients pre-aligned and facilitating coordinate-based parameter averaging. 位置感知神经元（PANs）将位置相关的值（即位置编码）融合到神经元输出中，使各客户的参数预先对齐，并促进基于坐标的参数平均化。 ↩
Federated semi-supervised learning (FSSL) aims to derive a global model by training fully-labeled and fully-unlabeled clients or training partially labeled clients. RSCFed presents a Random Sampling Consensus Federated learning, by considering the uneven reliability among models from fully-labeled clients, fully-unlabeled clients or partially labeled clients. 联邦半监督学习（FSSL）旨在通过训练有监督和无监督的客户或半监督的客户来得出一个全局模型。随机抽样共识联邦学习，即RSCFed，考虑来自有监督的客户、无监督的客户或半监督的客户的模型之间不均匀的可靠性。 ↩
FCCL (Federated Cross-Correlation and Continual Learning) For heterogeneity problem, FCCL leverages unlabeled public data for communication and construct cross-correlation matrix to learn a generalizable representation under domain shift. Meanwhile, for catastrophic forgetting, FCCL utilizes knowledge distillation in local updating, providing inter and intra domain information without leaking privacy. FCCL（联邦交叉相关和持续学习）对于异质性问题，FCCL利用未标记的公共数据进行交流，并构建交叉相关矩阵来学习领域转移下的可泛化表示。同时，对于灾难性遗忘，FCCL利用局部更新中的知识提炼，在不泄露隐私的情况下提供域间和域内信息。 ↩
RHFL (Robust Heterogeneous Federated Learning) simultaneously handles the label noise and performs federated learning in a single framework. RHFL（稳健模型异构联邦学习），它同时处理标签噪声并在一个框架内执行联邦学习。 ↩
ResSFL, a Split Federated Learning Framework that is designed to be MI-resistant during training. ResSFL一个分割学习的联邦学习框架，它被设计成在训练期间可以抵抗MI模型逆向攻击。 Model Inversion (MI) attack 模型逆向攻击。 ↩
FedDC propose a novel federated learning algorithm with local drift decoupling and correction. FedDC 一种带有本地漂移解耦和校正的新型联邦学习算法。 ↩
Global-Local Forgetting Compensation (GLFC) model, to learn a global class incremental model for alleviating the catastrophic forgetting from both local and global perspectives. 全局-局部遗忘补偿（GLFC）模型，从局部和全局的角度学习一个全局类增量模型来缓解灾难性的遗忘问题。 ↩
FedFTG, a data-free knowledge distillation method to fine-tune the global model in the server, which relieves the issue of direct model aggregation. FedFTG, 一种无数据的知识蒸馏方法来微调服务器中的全局模型，它缓解了直接模型聚合的问题。 ↩
DP-FedAvg+BLUR+LUS study the cause of model performance degradation in federated learning under user-level DP guarantee and propose two techniques, Bounded Local Update Regularization and Local Update Sparsification, to increase model quality without sacrificing privacy. DP-FedAvg+BLUR+LUS 研究了在用户级DP保证下联邦学习中模型性能下降的原因,提出了两种技术，即有界局部更新正则化和局部更新稀疏化，以提高模型质量而不牺牲隐私。 ↩
Generative Gradient Leakage (GGL) validate that the private training data can still be leaked under certain defense settings with a new type of leakage. 生成梯度泄漏（GGL）验证了在某些防御设置下，私人训练数据仍可被泄漏。 ↩
CD2-pFed, a novel Cyclic Distillation-guided Channel Decoupling framework, to personalize the global model in FL, under various settings of data heterogeneity. CD2-pFed，一个新的循环蒸馏引导的通道解耦框架，在各种数据异质性的设置下，在FL中实现全局模型的个性化。 ↩
FedSM propose a novel training framework to avoid the client drift issue and successfully close the generalization gap compared with the centralized training for medical image segmentation tasks for the first time. 新的训练框架FedSM，以避免客户端漂移问题，并首次成功地缩小了与集中式训练相比在医学图像分割任务中的泛化差距。 ↩
FL-MRCM propose a federated learning (FL) based solution in which we take advantage of the MR data available at different institutions while preserving patients' privacy. FL-MRCM 一个基于联邦学习（FL）的解决方案，其中我们利用了不同机构的MR数据，同时保护了病人的隐私。 ↩
MOON: model-contrastive federated learning. MOON is to utilize the similarity between model representations to correct the local training of individual parties, i.e., conducting contrastive learning in model-level. MOON 模型对比学习。MOON的关键思想是利用模型表征之间的相似性来修正各方的局部训练，即在模型层面进行对比学习。 ↩
FedDG-ELCFS A novel problem setting of federated domain generalization (FedDG), which aims to learn a federated model from multiple distributed source domains such that it can directly generalize to unseen target domains. Episodic Learning in Continuous Frequency Space (ELCFS), for this problem by enabling each client to exploit multi-source data distributions under the challenging constraint of data decentralization. FedDG-ELCFS 联邦域泛化（FedDG）旨在从多个分布式源域中学习一个联邦模型，使其能够直接泛化到未见过的目标域中。连续频率空间中的偶发学习（ELCFS），使每个客户能够在数据分散的挑战约束下利用多源数据分布。 ↩
Soteria propose a defense against model inversion attack in FL, learning to perturb data representation such that the quality of the reconstructed data is severely degraded, while FL performance is maintained. Soteria 一种防御FL中模型反转攻击的方法,关键思想是学习扰乱数据表示，使重建数据的质量严重下降，而FL性能保持不变。 ↩
FedUFO a Unified Feature learning and Optimization objectives alignment method for non-IID FL. FedUFO 一种针对non IID FL的统一特征学习和优化目标对齐算法。 ↩
FedAD propose a new distillation-based FL frame-work that can preserve privacy by design, while also consuming substantially less network communication resources when compared to the current methods. FedAD 一个新的基于蒸馏的FL框架，它可以通过设计来保护隐私，同时与目前的方法相比，消耗的网络通信资源也大大减少 ↩
FedU a novel federated unsupervised learning framework. FedU 一个新颖的无监督联邦学习框架. ↩
FedUReID, a federated unsupervised person ReID system to learn person ReID models without any labels while preserving privacy. FedUReID，一个联邦的无监督人物识别系统，在没有任何标签的情况下学习人物识别模型，同时保护隐私。 ↩
Introduce two new large-scale datasets for species and landmark classification, with realistic per-user data splits that simulate real-world edge learning scenarios. We also develop two new algorithms (FedVC, FedIR) that intelligently resample and reweight over the client pool, bringing large improvements in accuracy and stability in training. 为物种和地标分类引入了两个新的大规模数据集，每个用户的现实数据分割模拟了真实世界的边缘学习场景。我们还开发了两种新的算法（FedVC、FedIR），在客户池上智能地重新取样和重新加权，在训练中带来了准确性和稳定性的巨大改进 ↩
InvisibleFL propose a privacy-preserving solution that avoids multimedia privacy leakages in federated learning. InvisibleFL 提出了一个保护隐私的解决方案，以避免联邦学习中的多媒体隐私泄漏。 ↩
FedReID implement federated learning to person re-identification and optimize its performance affected by statistical heterogeneity in the real-world scenario. FedReID 实现了对行人重识别任务的联邦学习，并优化了其在真实世界场景中受统计异质性影响的性能。 ↩
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In this perspective paper we study the effect of non independent and identically distributed (non-IID) data on federated online learning to rank (FOLTR) and chart directions for future work in this new and largely unexplored research area of Information Retrieval. 在这篇前瞻论文中，我们研究了非独立和相同分布（非IID）数据对联邦在线学习排名（FOLTR）的影响，并为这个新的、基本上未被开发的信息检索研究领域的未来工作指明了方向。 ↩
The cross-domain recommendation problem is formalized under a decentralized computing environment with multiple domain servers. And we identify two key challenges for this setting: the unavailability of direct transfer and the heterogeneity of the domain-specific user representations. We then propose to learn and maintain a decentralized user encoding on each user's personal space. The optimization follows a variational inference framework that maximizes the mutual information between the user's encoding and the domain-specific user information from all her interacted domains. 跨域推荐问题在具有多个域服务器的去中心化计算环境下被形式化。我们确定了这种情况下的两个关键挑战：直接传输的不可用性和特定领域用户表征的异质性。然后，我们建议在每个用户的个人空间上学习和维护一个分散的用户编码。优化遵循一个变分推理框架，使用户的编码和来自她所有互动领域的特定用户信息之间的互信息最大化。 ↩
Under some circumstances, the private data can be reconstructed from the model parameters, which implies that data leakage can occur in FL.In this paper, we draw attention to another risk associated with FL: Even if federated algorithms are individually privacy-preserving, combining them into pipelines is not necessarily privacy-preserving. We provide a concrete example from genome-wide association studies, where the combination of federated principal component analysis and federated linear regression allows the aggregator to retrieve sensitive patient data by solving an instance of the multidimensional subset sum problem. This supports the increasing awareness in the field that, for FL to be truly privacy-preserving, measures have to be undertaken to protect against data leakage at the aggregator. 在某些情况下，私人数据可以从模型参数中重建，这意味着在联邦学习中可能发生数据泄漏。在本文中，我们提请注意与FL相关的另一个风险。即使联邦算法是单独保护隐私的，将它们组合成管道也不一定是保护隐私的。我们提供了一个来自全基因组关联研究的具体例子，其中联邦主成分分析和联邦线性回归的组合允许聚合器通过解决多维子集和问题的实例来检索敏感的病人数据。这支持了该领域日益增长的意识，即为了使FL真正保护隐私，必须采取措施防止聚合器的数据泄漏。 ↩
The federated cross-modal retrieval (FedCMR), which learns the model with decentralized multi-modal data. 联邦跨模式检索（FedCMR），它用分散的多模式数据学习模型。 ↩
A federated matrix factorization (MF) framework, named meta matrix factorization (MetaMF) for rating prediction (RP) for mobile environments. 一个联邦矩阵分解（MF）框架，命名为元矩阵分解（MetaMF），用于移动环境的评级预测（RP）。 ↩
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PAPAYA outline a production asynchronous FL system design. Empirically, we demonstrate that asynchronous FL converges faster than synchronous FL when training across nearly one hundred million devices. In particular, in high concurrency settings, asynchronous FL is 5x faster and has nearly 8x less communication overhead than synchronous FL. PAPAYA概述了一个生产性的异步联邦系统设计。根据经验，我们证明了在近一亿台设备上进行训练时，异步FL比同步FL收敛得更快。特别是，在高并发环境下，异步FL比同步FL快5倍，通信开销少8倍。 ↩
State-of-the-art secure aggregation protocols rely on secret sharing of the random-seeds used for mask generations at the users to enable the reconstruction and cancellation of those belonging to the dropped users. The complexity of such approaches, however, grows substantially with the number of dropped users. LightSecAgg, to overcome this bottleneck by changing the design from "random-seed reconstruction of the dropped users" to "one-shot aggregate-mask reconstruction of the active users via mask encoding/decoding". 最先进的安全聚合协议依赖于在用户处秘密共享用于掩码生成的随机种子，以便能够重建和取消属于被放弃用户的随机种子。然而，这种方法的复杂性随着被放弃的用户数量的增加而大大增加。LightSecAgg 通过将设计从 "被放弃用户的随机种子重建 "改为 "通过掩码编码/解码对活跃用户进行一次性聚合掩码重建 "来克服这个瓶颈。 ↩
Oort, improve the performance of federated training and testing with guided participant selection. With an aim to improve time-to-accuracy performance in model training, Oort prioritizes the use of those clients who have both data that offers the greatest utility in improving model accuracy and the capability to run training quickly. To enable FL developers to interpret their results in model testing, Oort enforces their requirements on the distribution of participant data while improving the duration of federated testing by cherry-picking clients. Oort，通过指导性的参与者选择来提高联邦训练和测试的性能。为了提高模型训练的时间-精度性能，Oort优先使用那些既拥有对提高模型精度有最大作用的数据又有能力快速运行训练的客户。为了使FL开发者能够解释他们在模型测试中的结果，Oort强制执行他们对参与者数据分布的要求，同时通过挑选客户来改善联邦测试的持续时间。 ↩
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Model the fairness guaranteed client selection as a Lyapunov optimization problem and then a C2MAB-based method is proposed for estimation of the model exchange time between each client and the server, based on which we design a fairness guaranteed algorithm termed RBCS-F for problem-solving. 我们将保证公平性的客户选择建模为一个Lyapunov优化问题，然后提出一个基于C2MAB的方法来估计每个客户和服务器之间的模型交换时间，在此基础上，我们设计了一个保证公平性的算法，即RBCS-F来解决问题。 ↩
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TRUDA, a new cross-silo FL system, employing a trustworthy and decentralized aggregation architecture to break down information concentration with regard to a single aggregator. Based on the unique computational properties of model-fusion algorithms, all exchanged model updates in TRUDA are disassembled at the parameter-granularity and re-stitched to random partitions designated for multiple TEE-protected aggregators. TRUDA是一个新的跨机构FL系统，采用了一个可信的、分散的聚合架构，以打破对单一聚合器的信息集中。基于模型融合算法的独特计算特性，TRUDA中所有交换的模型更新都在参数粒度上被分解，并重新缝合到指定给多个受TEE保护的聚合器的随机分区。 ↩
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FedProx, to tackle heterogeneity in federated networks. FedProx can be viewed as a generalization and re-parametrization of FedAvg, the current state-of-the-art method for federated learning. While this re-parameterization makes only minor modifications to the method itself, these modifications have important ramifications both in theory and in practice. Theoretically, we provide convergence guarantees for our framework when learning over data from non-identical distributions (statistical heterogeneity), and while adhering to device-level systems constraints by allowing each participating device to perform a variable amount of work (systems heterogeneity). Practically, we demonstrate that FedProx allows for more robust convergence than FedAvg across a suite of realistic federated datasets. FedProx，解决联邦网络中的异质性问题。FedProx可以被看作是FedAvg的概括和重新参数化，FedAvg是目前最先进的联邦学习方法。虽然这种重新参数化只对方法本身做了微小的修改，但这些修改在理论和实践上都有重要的影响。在理论上，我们为我们的框架提供了收敛保证，当对来自非相同分布的数据进行学习时（统计异质性），同时通过允许每个参与的设备执行不同数量的工作（系统异质性）来遵守设备级别的系统约束。在实践中，我们证明了FedProx比FedAvg在一系列现实的联邦数据集中能实现更稳健的收敛。 ↩
We have built a scalable production system for Federated Learning in the domain of mobile devices, based on TensorFlow. In this paper, we describe the resulting high-level design, sketch some of the challenges and their solutions, and touch upon the open problems and future directions. 我们已经为移动设备领域的联邦学习建立了一个可扩展的生产系统，基于TensorFlow。在本文中，我们描述了由此产生的高层次设计，概述了一些挑战和它们的解决方案，并谈到了开放的问题和未来的方向。 ↩

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