Flower: A Friendly Federated AI Framework

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

A flexible Federated Learning Framework based on PyTorch, simplifying your Federated Learning research.

All materials you need for Federated Learning: blogs, videos, papers, and softwares, etc.

Benchmark of federated learning. Dedicated to the community. 🤗

The first open Federated Learning framework implemented in C++ and Python.

Simulation framework for accelerating research in Private Federated Learning

Low-level Python library used to interact with a Substra network

Galaxy Federated Learning Framework （星际联邦学习框架）

FedGraphNN: A Federated Learning Platform for Graph Neural Networks with MLOps Support. The previous research version is accepted to ICLR'2021 - DPML and MLSys'21 - GNNSys workshops.

Advanced Privacy-Preserving Federated Learning framework

⚔️ Blades: A Unified Benchmark Suite for Attacks and Defenses in Federated Learning

HeFlwr: Federated Learning for Heterogeneous Devices

NEBULA: A Platform for Decentralized Federated Learning

Simulation Codes for "Reconfigurable Intelligent Surface Enabled Federated Learning: A Unified Communication-Learning Design Approach"

A flexible, modular, and easy to use library to facilitate federated learning research and development in healthcare settings

Nerlnet is a framework for research and development of distributed machine learning models on IoT

An Efficient and Easy-to-use Federated Learning Framework.

[ICLR2023] Towards Understanding and Mitigating Dimensional Collapse in Heterogeneous Federated Learning (https://arxiv.org/abs/2210.00226)

Federated learning with homomorphic encryption enables multiple parties to securely co-train artificial intelligence models in pathology and radiology, reaching state-of-the-art performance with privacy guarantees.

🔨 A Flexible Federated Learning Simulator for Heterogeneous and Asynchronous.

Federated Learning (FL) experiment simulation in Python.

FedRay: a Research Framework for Federated Learning based on Ray

Flexe - The open source federated learning for vehicular network simulation framework.

(CVPR 2024) Official Implementation of "FedSOL: Stabilized Orthogonal Learning with Proximal Restrictions in Federated Learning"

FedEasy is an intuitive powerful yet simple to use Federated Learning framework. Our goal is to streamline the process of setting up and running federated learning experiments with ease, making advanced machine learning techniques accessible to researchers and developers alike.

A Docker-Based Federated Learning Framework Design and Deployment for Multi-modal Data Stream Classification

Simulation Codes for “Relay-Assisted Cooperative Federated Learning”

Flexe - The open source federated learning for vehicular network simulation framework.

SemiPFL: Personalized Semi-Supervised Federated Learning Framework for Edge Intelligence