CVPR 2024 accepted paper, An Upload-Efficient Scheme for Transferring Knowledge From a Server-Side Pre-trained Generator to Clients in Heterogeneous Federated Learning
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Updated
Nov 11, 2024 - Python
CVPR 2024 accepted paper, An Upload-Efficient Scheme for Transferring Knowledge From a Server-Side Pre-trained Generator to Clients in Heterogeneous Federated Learning
AAAI 2024 accepted paper, FedTGP: Trainable Global Prototypes with Adaptive-Margin-Enhanced Contrastive Learning for Data and Model Heterogeneity in Federated Learning
KDD 2023 accepted paper, FedCP: Separating Feature Information for Personalized Federated Learning via Conditional Policy
NeurIPS 2023 accepted paper, Eliminating Domain Bias for Federated Learning in Representation Space
ICCV 2023 accepted paper, GPFL: Simultaneously Learning Global and Personalized Feature Information for Personalized Federated Learning
FedAnil is a secure blockchain-enabled Federated Deep Learning Model to address non-IID data and privacy concerns. This repo hosts a simulation for FedAnil written in Python.
testing adhocSL
FedAnil+ is a novel lightweight, and secure Federated Deep Learning Model to address non-IID data, privacy concerns, and communication overhead. This repo hosts a simulation for FedAnil+ written in Python.
FedAnil++ is a Privacy-Preserving and Communication-Efficient Federated Deep Learning Model to address non-IID data, privacy concerns, and communication overhead. This repo hosts a simulation for FedAnil++ written in Python.
a semi-synchronous Federated Learning method (LESSON) for hetrogenous wireless clients with non-iid data distribution
This is the PyTorch implementation of our paper "Jie Du, Wei Li, Peng Liu, et al. Model projection based Federated Learning for Non-IID data" .
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