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
Simulation Codes for "Reconfigurable Intelligent Surface Enabled Federated Learning: A Unified Communication-Learning Design Approach"
NEBULA: A Platform for Decentralized Federated Learning
Nerlnet is a framework for research and development of distributed machine learning models on IoT
A flexible, modular, and easy to use library to facilitate federated learning research and development in healthcare settings
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.
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