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
Mar 12, 2025 - 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 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.
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.
This repository is PyTorch implementation for paper CADIS: Handling Cluster-skewed Non-IID Data in Federated Learning with Clustered Aggregation and Knowledge DIStilled Regularization which is accepted by CCGRID-23 Conference.
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" .
a semi-synchronous Federated Learning method (LESSON) for hetrogenous wireless clients with non-iid data distribution
Generate and download free synthetic datasets instantly! A Streamlit app with built-in statistical validation tools like Chi-Square and Mutual Information.
The project aims to explore Federated Learning in scenarios with non-IID data (non-Independent and Identically Distributed) for the task of movie recommendation.
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