Popular GitHub Repositories for Heterogeneity

Master Federated Learning in 2 Hours—Run It on Your PC!

You only need to configure one file to support model heterogeneity. Consistent GPU memory usage for single or multiple clients.

Code for paper: DGR-MIL: Exploring Diverse Global Representation in Multiple Instance Learning for Whole Slide Image Classification [ECCV 2024]

[ACM MobiCom 2022] "PyramidFL: Fine-grained Data and System Heterogeneity-aware Client Selection for Efficient Federated Learning" by Chenning Li, Xiao Zeng, Mi Zhang, and Zhichao Cao.

This is a platform containing the datasets and federated learning algorithms in IoT environments.

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

Heterogeneous Multi-Robot Reinforcement Learning

Official implementation of CVPR'24 paper 'Anomaly Heterogeneity Learning for Open-set Supervised Anomaly Detection'.

Papers related to Federated Learning in all top venues

Pytorch implementation of our paper accepted by CVPR 2022 -- IntraQ: Learning Synthetic Images with Intra-Class Heterogeneity for Zero-Shot Network Quantization

[ICLR 2023] "Combating Exacerbated Heterogeneity for Robust Models in Federated Learning"

Efficient and Straggler-Resistant Homomorphic Encryption for Heterogeneous Federated Learning

KDD 2023 accepted paper, FedCP: Separating Feature Information for Personalized Federated Learning via Conditional Policy

Bayesian mixture models for estimating and clustering cancer cell fractions

[Official] NeurIPS 2023, "Navigating Data Heterogeneity in Federated Learning: A Semi-Supervised Approach for Object Detection"

ICCV 2023 accepted paper, GPFL: Simultaneously Learning Global and Personalized Feature Information for Personalized Federated Learning

WIP. Veloce is a low-code Ray-based parallelization library that makes machine learning computation novel, efficient, and heterogeneous.

Adaptive Guidance for Local Training in Heterogeneous Federated Learning

This repository contains code for the paper "Stateful Active Facilitator: Coordination and Environmental Heterogeneity in Cooperative Multi-Agent Reinforcement Learning". https://arxiv.org/abs/2210.03022