Toolbox for federated learning research.

Reproduce CKA: Similarity of Neural Network Representations Revisited

This repository is an official PyTorch implementation of paper: Rethinking the Representation in Federated Unsupervised Learning with Non-IID Data. CVPR 2024.

TPAMI2023 & CVPR2022 - Generalizable Heterogeneous Federated Cross-Correlation and Instance Similarity Learning & Learn From Others and Be Yourself in Heterogeneous Federated Learning

Federated Learning for LLMs.

[EMNLP'24] EHRAgent: Code Empowers Large Language Models for Complex Tabular Reasoning on Electronic Health Records

Ongoing research training transformer models at scale

MedAlign is a clinician-generated dataset for instruction following with electronic medical records.

PyTorch native finetuning library

The original code for the paper "How to train your MAML" along with a replication of the original "Model Agnostic Meta Learning" (MAML) paper in Pytorch.

FedMSE: Semi-supervised federated learning approach for IoT network intrusion detection

[ICLR 2024] "FedImpro: Measuring and Improving Client Update in Federated Learning"

ConvNet training using pytorch

ICML2022: Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learning

Official repository for Local Learning Matters: Rethinking Data Heterogeneity in Federated Learning [CVPR 2022 Oral, Best Paper Finalist]

Model-Contrastive Federated Learning (CVPR 2021)

Code release for Tackling Data Heterogeneity in Federated Learning with Class Prototypes appeared on AAAI2023.

[NeurIPS 2023] "FedFed: Feature Distillation against Data Heterogeneity in Federated Learning"

[ICLR 2024] "Enhancing One-Shot Federated Learning Through Data and Ensemble Co-Boosting"

[ICLR 2024] "Robust Training of Federated Models with Extremely Label Deficiency"

[NeurIPS 2023] "FedFed: Feature Distillation against Data Heterogeneity in Federated Learning"

This is the official impelementation of "FVSSL Algorithm"

In this project, we propose to study Vision Transformers trained using the Barlow Twins self-supervised method, and compare the results with DINO. We demonstrate the effectiveness of the Barlow Twi…

An easy-to-use federated learning platform

self-supervised domain adaptation

Comprehensive and timely academic information on federated learning (papers, frameworks, datasets, tutorials, workshops)

Self-supervised federated learning for medical imaging - IEEE TMI

Everything about federated learning, including research papers, books, codes, tutorials, videos and beyond

Learning Across Domains and Devices: Style-Driven Source-Free Domain Adaptation in Clustered Federated Learning