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

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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 conferences
    • AI ML DM Secure CV NLP
• Framework
• Datasets

Papers

keywords

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

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

Update log

• 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 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	Shanghai Jiao Tong University	KDD 🎓	2022	FedWalk1	[PDF]
EasyFGL: Towards a Unified, Comprehensive and Efficient Platform for Federated Graph Learning 🔥	Alibaba Group	KDD 🎓	2022	FederatedScope-GNN 2	[PDF] Code
Deep Neural Network Fusion via Graph Matching with Applications to Model Ensemble and Federated Learning		ICML 🎓	2022		[PUB.] [Code]
Meta-Learning Based Knowledge Extrapolation for Knowledge Graphs in the Federated Setting kg.		IJCAI 🎓	2022		[PDF] [Code]
SpreadGNN: Serverless Multi-task Federated Learning for Graph Neural Networks		AAAI 🎓	2022		[PDF] [Code] [Interpret(zh)]
A federated graph neural network framework for privacy-preserving personalization		Nature Communications	2022		[PUB.]
Efficient Federated Learning on Knowledge Graphs via Privacy-preserving Relation Embedding Aggregation kg.		ACL Workshop	2022		[PDF] [Code]
Power Allocation for Wireless Federated Learning using Graph Neural Networks		ICASSP	2022		[PDF] [PUB.] [Code]
Privacy-Preserving Federated Multi-Task Linear Regression: A One-Shot Linear Mixing Approach Inspired By Graph Regularization		ICASSP	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		[PDF] [PUB.]
Graph-Based Traffic Forecasting via Communication-Efficient Federated Learning		WCNC	2022		[PUB.]
Malicious Transaction Identification in Digital Currency via Federated Graph Deep Learning		INFOCOM Workshops	2022		[PUB.]
Federated learning of molecular properties with graph neural networks in a heterogeneous setting		Patterns	2022		[PUB.]
Decentralized Graph Federated Multitask Learning for Streaming Data		CISS	2022		[PUB.] [[Interpret(zh)]]
Dynamic Neural Graphs Based Federated Reptile for Semi-Supervised Multi-Tasking in Healthcare Applications		JBHI	2022		[PDF]
Federated Knowledge Graphs Embedding kg.		CIKM	2021		[PDF] [Code] [Interpret(zh)]
Federated Graph Classification over Non-IID Graphs		NeurIPS 🎓	2021		[PDF] [PUB.] [Code] [Interpret(zh)]
Decentralized Federated Graph Neural Networks		IJCAI Workshop	2021		[PDF]
FL-DISCO: Federated Generative Adversarial Network for Graph-based Molecule Drug Discovery: Special Session Paper		ICCAD	2021		[PUB.]
DAG-FL: Direct Acyclic Graph-based Blockchain Empowers On-Device Federated Learning		ICC	2021		[PUB.]
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		[PDF] [PUB.][Code]
Federated Knowledge Graph Embeddings with Heterogeneous Data kg.		CCKS	2021		[PUB.]
A Graph Federated Architecture with Privacy Preserving Learning		SPAWC	2021		[PDF] [PUB.]
Federated Social Recommendation with Graph Neural Network		ACM TIST	2021		[PDF]
Subgraph Federated Learning with Missing Neighbor Generation		NeurIPS 🎓	2021		[PDF] [PUB.]
Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modeling		KDD 🎓	2021		[PDF] [Code]
FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks 🔥 surv.		ICLR-DPML	2021		[PDF] [Code] [Interpret(zh)]
Cluster-driven Graph Federated Learning over Multiple Domains		CVPR Workshop	2021		[PDF] [Interpret(zh)]
Differentially Private Federated Knowledge Graphs Embedding kg.		CIKM	2021		[PDF] [Code]
Glint: Decentralized Federated Graph Learning with Traffic Throttling and Flow Scheduling		IWQoS	2021		[PUB.]
A Federated Multigraph Integration Approach for Connectional Brain Template Learning		MICCAI Workshop	2021		[PDF]
FedGraph: Federated Graph Learning with Intelligent Sampling		TPDS 🎓	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		[PUB.]
Distributed Training of Graph Convolutional Networks		TSIPN	2021		[PUB.] [PDF] [[Interpret(zh)]]
ASFGNN: Automated Separated-Federated Graph Neural Network		PPNA	2020		[PDF] [PUB.] [Interpret(zh)]
Towards Federated Graph Learning for Collaborative Financial Crimes Detection		NeurIPS Workshop	2019		[PDF]
FedGNN: Federated Graph Neural Network for Privacy-Preserving Recommendation		ICML workshop	2021		[PDF] [Interpret(zh)]
SGNN: A Graph Neural Network Based Federated Learning Approach by Hiding Structure		BigData	2019		[PDF] [PUB.]
Federated Graph Machine Learning: A Survey of Concepts, Techniques, and Applications surv.	University of Virginia	preprint	2022	FGML 3	[PDF]
FD-GATDR: A Federated-Decentralized-Learning Graph Attention Network for Doctor Recommendation Using EHR		preprint	2022		[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]
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		[PDF] [Code]
Federated Learning with Heterogeneous Architectures using Graph HyperNetworks		preprint	2022		[PDF]
FedNI: Federated Graph Learning with Network Inpainting for Population-Based Disease Prediction		preprint	2021		[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]
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		[PDF]
FedGL: Federated Graph Learning Framework with Global Self-Supervision		preprint	2021		[PDF]
FL-AGCNS: Federated Learning Framework for Automatic Graph Convolutional Network Search		preprint	2021		[PDF]
Towards On-Device Federated Learning: A Direct Acyclic Graph-based Blockchain Approach		preprint	2021		[PDF]
GraphFL: A Federated Learning Framework for Semi-Supervised Node Classification on Graphs		preprint	2020		[PDF]
Improving Federated Relational Data Modeling via Basis Alignment and Weight Penalty kg.		preprint	2020		[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		preprint	2019		[PDF]

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 Random Forests can improve local performance of predictive models for various healthcare applications		Bioinform.	2022		[PUB.] [Code]
Federated Forest		TBD	2022		[PDF] [PUB.]
Federated Functional Gradient Boosting		AISTATS	2022		[PDF] [PUB.] [Code]
Fed-GBM: a cost-effective federated gradient boosting tree for non-intrusive load monitoring		e-Energy	2022		[PUB.]
eFL-Boost: Efficient Federated Learning for Gradient Boosting Decision Trees		IEEE Access	2022		[PUB.]
Random Forest Based on Federated Learning for Intrusion Detection		AIAI	2022		[PUB.]
Cross-silo federated learning based decision trees		SAC	2022		[PUB.]
Leveraging Spanning Tree to Detect Colluding Attackers in Federated Learning		INFCOM Workshops	2022		[PUB.]
VF2Boost: Very Fast Vertical Federated Gradient Boosting for Cross-Enterprise Learning		SIGMOD 🎓	2021		[PUB.]
An Efficiency-Boosting Client Selection Scheme for Federated Learning With Fairness Guarantee		TPDS 🎓	2021		[PDF] [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		IEEE BigData	2021		[PDF] [PUB.]
Gradient Boosting Forest: a Two-Stage Ensemble Method Enabling Federated Learning of GBDTs		ICONIP	2021		[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		SEAA	2021		[PUB.]
Compression Boosts Differentially Private Federated Learning		EuroS&P	2021		[PDF] [PUB.]
Practical Federated Gradient Boosting Decision Trees		AAAI 🎓	2020		[PDF] [PUB.] [Code]
Boosting Privately: Federated Extreme Gradient Boosting for Mobile Crowdsensing		ICDCS	2020		[PDF] [PUB.]
FedCluster: Boosting the Convergence of Federated Learning via Cluster-Cycling		IEEE BigData	2020		[PDF] [PUB.]
Bandwidth Slicing to Boost Federated Learning Over Passive Optical Networks		IEEE Communications Letters	2020		[PUB.]
Privacy Preserving Vertical Federated Learning for Tree-based Models		Proc. VLDB Endow.	2020		[PDF] [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		[PDF]
FedGBF: An efficient vertical federated learning framework via gradient boosting and bagging		preprint	2022		[PDF]
An Efficient and Robust System for Vertically Federated Random Forest		preprint	2022		[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		[PDF] [Code]
Fed-TGAN: Federated Learning Framework for Synthesizing Tabular Data		preprint	2021		[PDF]
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		preprint	2020		[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]

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.]
Communication-efficient federated learning via knowledge distillation		Nat Commun	2022		[PUB.]
A federated graph neural network framework for privacy-preserving personalization		Nat Commun	2022		[PUB.] [Code]
Swarm Learning for decentralized and confidential clinical machine learning		Nature 🎓	2021		[PUB.]
Adversarial interference and its mitigations in privacy-preserving collaborative machine learning		Nat. Mach. Intell.	2021		[PUB.]
End-to-end privacy preserving deep learning on multi-institutional medical imaging		Nat. Mach. Intell.	2021		[PUB.]
Federated learning for predicting clinical outcomes in patients with COVID-19		Nat Med	2021		[PUB.]
Communication-efficient federated learning		PANS	2021		[PUB.] [Code]
Advancing COVID-19 diagnosis with privacy-preserving collaboration in artificial intelligence		Nat. Mach. Intell.	2021		[PUB.]
Secure, privacy-preserving and federated machine learning in medical imaging		Nat Mach Intell	2020		[PUB.]
Breaking medical data sharing boundaries by using synthesized radiographs		Science Advances	2020		[PUB.]

FL in top AI Conferences

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

• AAAI 2022,2021,2020
• AISTATS 2022, 2021,2020

Title	Affiliation	Venue	Year	TL;DR	Materials
HarmoFL: Harmonizing Local and Global Drifts in Federated Learning on Heterogeneous Medical Images	The Chinese University of Hong Kong; Beihang University	AAAI	2022		[Code]
Federated Learning for Face Recognition with Gradient Correction	Beijing University of Posts and Telecommunications	AAAI	2022
SpreadGNN: Decentralized Multi-Task Federated Learning for Graph Neural Networks on Molecular Data	university of Southern California	AAAI	2022		[Code]
SmartIdx: Reducing Communication Cost in Federated Learning by Exploiting the CNNs Structures	Harbin Institute of Technology; Peng Cheng Laboratory	AAAI	2022
Bridging between Cognitive Processing Signals and Linguistic Features via a Unified Attentional Network	Tianjin University	AAAI	2022
Seizing Critical Learning Periods in Federated Learning	SUNY-Binghamton University; Louisiana State University	AAAI	2022
Coordinating Momenta for Cross-silo Federated Learning	University of Pittsburgh	AAAI	2022
FedProto: Federated Prototype Learning over Heterogeneous Devices	University of Technology Sydney; University of Washington	AAAI	2022		[Code]
FedSoft: Soft Clustered Federated Learning with Proximal Local Updating	Carnegie Mellon University	AAAI	2022
Federated Dynamic Sparse Training: Computing Less, Communicating Less, Yet Learning Better	The University of Texas at Austin	AAAI	2022		[Code]
FedFR: Joint Optimization Federated Framework for Generic and Personalized Face Recognition	National Taiwan University	AAAI	2022		[Code]
SplitFed: When Federated Learning Meets Split Learning	CSIRO; Lehigh University	AAAI	2022		[Code]
Efficient Device Scheduling with Multi-Job Federated Learning	Soochow University; Baidu	AAAI	2022
Implicit Gradient Alignment in Distributed and Federated Learning	IIT Kanpur; EPFL	AAAI	2022
Federated Nearest Neighbor Classification with a Colony of Fruit-Flies	IBM Research; Wichita State University	AAAI	2022
Secure Bilevel Asynchronous Vertical Federated Learning with Backward Updating	Xidian University; JD Tech	AAAI	2021		video
FedRec++: Lossless Federated Recommendation with Explicit Feedback	Shenzhen University	AAAI	2021		video
Federated Multi-Armed Bandits	University of Virginia	AAAI	2021		[Code] video
On the Convergence of Communication-Efficient Local SGD for Federated Learning	Temple University; University of Pittsburgh	AAAI	2021		video
FLAME: Differentially Private Federated Learning in the Shuffle Model	Renmin University of China; Kyoto University	AAAI	2021		video [Code]
Toward Understanding the Influence of Individual Clients in Federated Learning	Shanghai Jiao Tong University; The University of Texas at Dallas	AAAI	2021		video
Provably Secure Federated Learning against Malicious Clients	Duke University	AAAI	2021		video slides
Personalized Cross-Silo Federated Learning on Non-IID Data	Simon Fraser University; McMaster University	AAAI	2021		video
Model-Sharing Games: Analyzing Federated Learning under Voluntary Participation	Cornell University	AAAI	2021		[Code] video
Curse or Redemption? How Data Heterogeneity Affects the Robustness of Federated Learning	University of Nevada; IBM Research	AAAI	2021		video
Game of Gradients: Mitigating Irrelevant Clients in Federated Learning	IIT Bombay; IBM Research	AAAI	2021		video Supplementary
Federated Block Coordinate Descent Scheme for Learning Global and Personalized Models	The Chinese University of Hong Kong; Arizona State University	AAAI	2021		video [Code]
Addressing Class Imbalance in Federated Learning	Northwestern University	AAAI	2021		video [Code] [[Interpret(zh)]]
Defending against Backdoors in Federated Learning with Robust Learning Rate	The University of Texas at Dallas	AAAI	2021		video [Code]
Free-rider Attacks on Model Aggregation in Federated Learning	Accenture Labs	AISTAT	2021		video Supplementary
Federated f-differential privacy	University of Pennsylvania	AISTAT	2021		[Code] video Supplementary
Federated learning with compression: Unified analysis and sharp guarantees 🔥	The Pennsylvania State University; The University of Texas at Austin	AISTAT	2021		[Code] video Supplementary
Shuffled Model of Differential Privacy in Federated Learning	UCLA; Google	AISTAT	2021		video Supplementary
Convergence and Accuracy Trade-Offs in Federated Learning and Meta-Learning	Google	AISTAT	2021		video Supplementary
Federated Multi-armed Bandits with Personalization	University of Virginia; The Pennsylvania State University	AISTAT	2021		[Code] video Supplementary
Towards Flexible Device Participation in Federated Learning	CMU; Sun Yat-Sen University	AISTAT	2021		video Supplementary
Practical Federated Gradient Boosting Decision Trees	National University of Singapore; The University of Western Australia	AAAI	2020		[Code]
Federated Learning for Vision-and-Language Grounding Problems	Peking University; Tencent	AAAI	2020
Federated Latent Dirichlet Allocation: A Local Differential Privacy Based Framework	Beihang University	AAAI	2020
Federated Patient Hashing	Cornell University	AAAI	2020
Robust Federated Learning via Collaborative Machine Teaching	Symantec Research Labs; KAUST	AAAI	2020
FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization	UC Santa Barbara; UT Austin	AISTAT	2020		video Supplementary
How To Backdoor Federated Learning 🔥	Cornell Tech	AISTAT	2020		video [Code] Supplementary
Federated Heavy Hitters Discovery with Differential Privacy	RPI; Google	AISTAT	2020		video Supplementary

FL in top ML conferences

In this section, we will summarize Federated Learning papers accepted by top ML(machine learning) conference, Including NeurIPS(Annual Conference on Neural Information Processing Systems), ICML(International Conference on Machine Learning), ICLR(International Conference on Learning Representations).

• NeurIPS 2021, 2020, 2018, 2017
• ICML 2022, 2021, 2020, 2019
• ICLR 2022,2021,2019

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

FL in top DM conferences

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

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

Title	Affiliation	Venue	Year	TL;DR	Materials
Collaboration Equilibrium in Federated Learning	Department of Automation, Tsinghua University	KDD	2022	CE4	[PDF] Code
Connected Low-Loss Subspace Learning for a Personalization in Federated Learning	Ulsan National Institute of Science and Technology	KDD	2022
FedMSplit: Correlation-Adaptive Federated Multi-Task Learning across Multimodal Split Networks	University of Virginia	KDD	2022
Communication-Efficient Robust Federated Learning with Noisy Labels	University of Pittsburgh	KDD	2022	Comm-FedBiO5	[PDF]
FLDetector: Detecting Malicious Clients in Federated Learning via Checking Model-Updates Consistency	University of Science and Technology of China	KDD	2022	FLDetector6	[PDF] Code
Practical Lossless Federated Singular Vector Decomposition Over Billion-Scale Data	HKUST	KDD	2022	FedSVD 7	[PDF] Code
FedWalk: Communication Efficient Federated Unsupervised Node Embedding with Differential Privacy	Shanghai Jiao Tong University	KDD	2022	FedWalk1	[PDF]
EasyFGL: Towards a Unified, Comprehensive and Efficient Platform for Federated Graph Learning 🔥	Alibaba Group	KDD	2022	FederatedScope-GNN 2	[PDF] Code
Fed-LTD: Towards Cross-Platform Ride Hailing via Federated Learning to Dispatch	Beihang University	KDD	2022	Fed-LTD 8	[PDF] [Interpret(zh)]
Felicitas: Federated Learning in Distributed Cross Device Collaborative Frameworks	USTC	KDD	2022		[PDF]
No One Left Behind: Inclusive Federated Learning over Heterogeneous Devices	Renmin University of China	KDD	2022	InclusiveFL 9	[PDF]
FedAttack: Effective and Covert Poisoning Attack on Federated Recommendation via Hard Sampling	Tsinghua University	KDD	2022	FedAttack 10	[PDF]
Fed2: Feature-Aligned Federated Learning	George Mason University; Microsoft; University of Maryland	KDD	2021		PDF
FedRS: Federated Learning with Restricted Softmax for Label Distribution Non-IID Data	Nanjing University	KDD	2021		Code
Federated Adversarial Debiasing for Fair and Trasnferable Representations	Michigan State University	KDD	2021		HomePage Code
Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modeling	University of Southern California	KDD	2021		[Code] [Interpret(zh)]
AsySQN: Faster Vertical Federated Learning Algorithms with Better Computation Resource Utilization	Xidian University;JD Tech	KDD	2021		PDF
FLOP: Federated Learning on Medical Datasets using Partial Networks	Duke University	KDD	2021		[Code]
FedFast: Going Beyond Average for Faster Training of Federated Recommender Systems	University College Dublin	KDD	2020		video
Federated Doubly Stochastic Kernel Learning for Vertically Partitioned Data	JD Tech	KDD	2020		PDF video

FL in top Secure conferences

In this section, we will summarize Federated Learning papers accepted by top Secure conferences, 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).

• S&P 2019
• CCS 2021, 2019
• USENIX Security 2022, 2020
• NDSS 2021

Title	Affiliation	Venue	Year	TL;DR	Materials
SIMC: ML Inference Secure Against Malicious Clients at Semi-Honest Cost	Microsoft Research	USENIX Security	2022		PUB. PDF code
Efficient Differentially Private Secure Aggregation for Federated Learning via Hardness of Learning with Errors	University of Vermont	USENIX Security	2022		PUB.
Label Inference Attacks Against Vertical Federated Learning	Zhejiang University	USENIX Security	2022		PUB. code
FLAME: Taming Backdoors in Federated Learning	Technical University of Darmstadt	USENIX Security	2022		PUB. PDF
Local and Central Differential Privacy for Robustness and Privacy in Federated Learning	University at Buffalo, SUNY	NDSS	2022		PDF Unofficial code
Interpretable Federated Transformer Log Learning for Cloud Threat Forensics	University of the Incarnate Word, TX, USA	NDSS	2022		Unofficial code
FedCRI: Federated Mobile Cyber-Risk Intelligence	Technical University of Darmstadt	NDSS	2022
DeepSight: Mitigating Backdoor Attacks in Federated Learning Through Deep Model Inspection	Technical University of Darmstadt	NDSS	2022		PDF
Private Hierarchical Clustering in Federated Networks	National University Of Singapore	CCS	2021
FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping	Duke University	NDSS	2021		PDF code Video Slides
POSEIDON: Privacy-Preserving Federated Neural Network Learning	Laboratory for Data Security, EPFL	NDSS	2021		Video
Manipulating the Byzantine: Optimizing Model Poisoning Attacks and Defenses for Federated Learning	University of Massachusetts Amherst	NDSS	2021		code Video
Local Model Poisoning Attacks to Byzantine-Robust Federated Learning	The Ohio State University	USENIX Security	2020		PDF code Video Slides
Poster: A Reliable and Accountable Privacy-Preserving Federated Learning Framework using the Blockchain	University of Kansas	CCS	2019
IOTFLA : A Secured and Privacy-Preserving Smart Home Architecture Implementing Federated Learning	Université du Québéc á Montréal	S&P	2019
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		code

FL in top CV conferences

In this section, we will summarize Federated Learning papers accepted by top CV(computer vision) conference, 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).

• CVPR 2022, 2021
• ICCV 2021
• ECCV 2020
• MM 2021, 2020

Title	Affiliation	Venue	Year	TL;DR	Materials
Personalizing Federated Medical Image Segmentation via Local Calibration	Xiamen University	ECCV	2022	LC-Fed 11	[PDF] Code
Improving Generalization in Federated Learning by Seeking Flat Minima	Politecnico di Torino	ECCV	2022	FedSAM 12	[PDF] Code
ATPFL: Automatic Trajectory Prediction Model Design Under Federated Learning Framework	Harbin Institute of Technology	CVPR	2022	ATPFL 13	[PUB.]
Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learning	Stanford University	CVPR	2022	ViT-FL 14	[PUB.] [supp] [PDF] [Code]
FedCorr: Multi-Stage Federated Learning for Label Noise Correction	Singapore University of Technology and Design	CVPR	2022	FedCorr15	[PUB.] [supp] [PDF] [Code]
FedCor: Correlation-Based Active Client Selection Strategy for Heterogeneous Federated Learning	Duke University	CVPR	2022	FedCor 16	[PUB.] [supp] [PDF]
Layer-Wised Model Aggregation for Personalized Federated Learning	The Hong Kong Polytechnic University	CVPR	2022	pFedLA 17	[PUB.] [supp] [PDF]
Local Learning Matters: Rethinking Data Heterogeneity in Federated Learning	University of Central Florida	CVPR	2022	FedAlign18	[PUB.] [supp] [PDF] [Code]
Federated Learning With Position-Aware Neurons	Nanjing University	CVPR	2022	PANs 19	[PUB.] [supp] [PDF]
RSCFed: Random Sampling Consensus Federated Semi-Supervised Learning	The Hong Kong University of Science and Technology	CVPR	2022	RSCFed 20	[PUB.] [supp] [PDF] [Code]
Learn From Others and Be Yourself in Heterogeneous Federated Learning	Wuhan University	CVPR	2022	FCCL21	[PUB.] [code]
Robust Federated Learning With Noisy and Heterogeneous Clients	Wuhan University	CVPR	2022	RHFL 22	[PUB.] [supp] [Code]
ResSFL: A Resistance Transfer Framework for Defending Model Inversion Attack in Split Federated Learning	Arizona State University	CVPR	2022	ResSFL 23	[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 24	[PUB.] [supp] [PDF] [Code] [Interpret(zh)]
Federated Class-Incremental Learning	Chinese Academy of Sciences; Northwestern University; University of Technology Sydney	CVPR	2022	GLFC 25	[PUB.] [supp] [PDF] [Code]
Fine-Tuning Global Model via Data-Free Knowledge Distillation for Non-IID Federated Learning	Peking University; JD Explore Academy; The University of Sydney	CVPR	2022	FedFTG 26	[PUB.] [supp] [PDF]
Differentially Private Federated Learning With Local Regularization and Sparsification	Chinese Academy of Sciences	CVPR	2022	DP-FedAvg +BLUR + LUS 27	[PUB.] [supp] [PDF]
Auditing Privacy Defenses in Federated Learning via Generative Gradient Leakage	University of Tennessee; Oak Ridge National Laboratory; Google Research	CVPR	2022	GGL 28	[PUB.] [supp] [PDF] [Code]
CD2-pFed: Cyclic Distillation-Guided Channel Decoupling for Model Personalization in Federated Learning	Shanghai Jiao Tong University	CVPR	2022	CD2-pFed 29	[PUB.] [PDF]
Closing the Generalization Gap of Cross-Silo Federated Medical Image Segmentation	Univ. of Pittsburgh; NVIDIA	CVPR	2022	FedSM 30	[PUB.] [supp] [PDF]
Multi-Institutional Collaborations for Improving Deep Learning-Based Magnetic Resonance Image Reconstruction Using Federated Learning	Johns Hopkins University	CVPR	2021	FL-MRCM 31	[Code]
Model-Contrastive Federated Learning 🔥	National University of Singapore; UC Berkeley	CVPR	2021	MOON 32	[pdf] [supp] [arXiv] [Code] [Interpret(zh)]
FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space 🔥	The Chinese University of Hong Kong	CVPR	2021	FedDG-ELCFS 33	[Code]
Soteria: Provable Defense Against Privacy Leakage in Federated Learning From Representation Perspective	Duke University	CVPR	2021	Soteria 34	[Code]
Federated Learning for Non-IID Data via Unified Feature Learning and Optimization Objective Alignment	Peking University	ICCV	2021	FedUFO 35
Ensemble Attention Distillation for Privacy-Preserving Federated Learning	University at Buffalo	ICCV	2021	FedAD 36	[PUB.]
Collaborative Unsupervised Visual Representation Learning from Decentralized Data	Nanyang Technological University; SenseTime	ICCV	2021	FedU 37	PDF
Joint Optimization in Edge-Cloud Continuum for Federated Unsupervised Person Re-identification	Nanyang Technological University	MM	2021	FedUReID 38	[PUB.] [PDF]
Federated Visual Classification with Real-World Data Distribution	MIT; Google	ECCV	2020	FedVC+FedIR 39	PDF Video
InvisibleFL: Federated Learning over Non-Informative Intermediate Updates against Multimedia Privacy Leakages		MM	2020	InvisibleFL 40	[PUB.]
Performance Optimization of Federated Person Re-identification via Benchmark Analysis data.	Nanyang Technological University	MM	2020	FedReID 41	[PUB.] [PDF] Code [Interpret(zh)]

FL in top NLP conferences

In this section, we will summarize Federated Learning papers accepted by top AI and NLP conference, 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).

• ACL 2022, 2021, 2019
• NAACL 2022, 2021
• EMNLP 2021, 2020
• COLING 2020

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

Framework

Federated Learning Framework

Platform	Papers	Affiliations	Graph data and algorithms	Tabular data and algorithms	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 Homepage
TFF(Tensorflow-Federated)	Towards Federated Learning at Scale: System Design	Google			Doc Homepage
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
LEAF	LEAF: A Benchmark for Federated Settings	CMU
FederatedScope	FederatedScope: A Flexible Federated Learning Platform for Heterogeneity	Alibaba DAMO Academy	✅✅		Doc Homepage
Rosetta		matrixelements			Doc Homepage
PaddleFL		Baidu			Doc
OpenFL	OpenFL: An open-source framework for Federated Learning	Intel			Doc
Privacy Meter	Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning	University of Massachusetts Amherst
IBM Federated Learning	IBM Federated Learning: an Enterprise Framework White Paper	IBM		✅	Papers
Fedlab	FedLab: A Flexible Federated Learning Framework	SMILELab			Doc Doc(zh) Homepage
NVFlare		NVIDIA			Doc
FedScale	FedScale: Benchmarking Model and System Performance of Federated Learning at Scale	SymbioticLab(U-M)
FedJAX	FEDJAX: Federated learning simulation with JAX	Google
Xaynet		XayNet			HomePage Doc Whitepaper Legal Review
plato		UofT
Galaxy Federated Learning	GFL: A Decentralized Federated Learning Framework Based On Blockchain	ZJU			Doc
FLSim		facebook research
PyVertical	PyVertical: A Vertical Federated Learning Framework for Multi-headed SplitNN	OpenMined
FedTorch	Distributionally Robust Federated Averaging	Penn State
9nfl		JD
FLUTE	FLUTE: A Scalable, Extensible Framework for High-Performance Federated Learning Simulations	microsoft			Doc
Breaching	A Framework for Attacks against Privacy in Federated Learning (papers)
FedLearn	Fedlearn-Algo: A flexible open-source privacy-preserving machine learning platform	JD
FEDn	Scalable federated machine learning with FEDn	scaleoutsystems			Doc
EasyFL	EasyFL: A Low-code Federated Learning Platform For Dummies	NTU
FedTree		Xtra Computing Group		✅✅	Doc
OpenFed	OpenFed: A Comprehensive and Versatile Open-Source Federated Learning Framework				Doc
FedEval	FedEval: A Benchmark System with a Comprehensive Evaluation Model for Federated Learning	HKU			Doc
Flame		Cisco
APPFL					Doc
Clara		NVIDIA

Datasets

(todo)

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

  • Awesome-Federated-Learning-on-Graph-and-GNN-papers
  • Awesome-GNN-Research
  • Awesome-Federated-Machine-Learning
  • Awesome-Federated-Learning
  • awesome-federated-learning
  • Federated-Learning
  • FLsystem-paper

• Other fields

  • anomaly-detection-resources
  • awesome-image-registration

Footnotes

1. 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

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

3. 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。 ↩

4. 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。 ↩

5. 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。 ↩

6. 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。 ↩

7. 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合SVD方法，可以同时实现无损精度和高效率。 ↩

8. 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。 ↩

9. 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。 ↩

10. 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是一种对联合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。 ↩

11. 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。 ↩

12. 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。 ↩

13. ATPFL helps users federate multi-source trajectory datasets to automatically design and train a powerful TP model. ATPFL帮助用户联合多源轨迹数据集，自动设计和训练强大的TP轨迹预测模型。 ↩

14. 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。 ↩

15. 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。 ↩

16. 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。 ↩

17. 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. "层级个性化联合学习"（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。 ↩

18. 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。 ↩

19. 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。 ↩

20. 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。 随机抽样共识联合学习，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。 ↩

21. 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。 ↩

22. 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。 ↩

23. 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 。 ↩

24. 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。 ↩

25. 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。 ↩

26. 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。 ↩

27. 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保证下联合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。 ↩

28. 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。 ↩

29. 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。 ↩

30. 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。 ↩

31. 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。 ↩

32. 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。 ↩

33. 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。 ↩

34. 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。 ↩

35. 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。 ↩

36. 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 ↩

37. FedU a novel federated unsupervised learning framework. FedU 一个新颖的无监督联邦学习框架. ↩

38. FedUReID, a federated unsupervised person ReID system to learn person ReID models without any labels while preserving privacy. FedUReID，一个联合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。 ↩

39. 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 ↩

40. 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，以避免联合学习中的多媒体隐私泄漏。 ↩

41. 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。 ↩