A flexible, modular, and easy to use library to facilitate federated learning research and development in healthcare settings
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Updated
Jun 12, 2024 - Python
A flexible, modular, and easy to use library to facilitate federated learning research and development in healthcare settings
Perform data science on data that remains in someone else's server
NAACL '24 (Demo) / MlSys @ NeurIPS '23 - RedCoast: A Lightweight Tool to Automate Distributed Training and Inference
FEDn: A production-grade, open federated learning framework. This repository contains the open source Python framework, CLI and API.
Flower: A Friendly Federated Learning Framework
🎓 Automatically Update Some Fields Papers Daily using Github Actions (Update Every 12th hours)
This repository contains the hub packages & services of FLAME.
Privacy-Preserving Computing Platform 由密码学专家团队打造的开源隐私计算平台,支持多方安全计算、联邦学习、隐私求交、匿踪查询等。
Cross-silo Federated Learning playground in Python. Discover 7 real-world federated datasets to test your new FL strategies and try to beat the leaderboard.
联邦学习模块化框架,支持各类FL。A universal federated learning framework, free to switch thread and process modes
A Comprehensive Survey of Forgetting in Deep Learning Beyond Continual Learning. arXiv:2307.09218.
Kuscia(Kubernetes-based Secure Collaborative InfrA) is a K8s-based privacy-preserving computing task orchestration framework.
A flexible Federated Learning Framework based on PyTorch, simplifying your Federated Learning research.
FEDML - The unified and scalable ML library for large-scale distributed training, model serving, and federated learning. FEDML Launch, a cross-cloud scheduler, further enables running any AI jobs on any GPU cloud or on-premise cluster. Built on this library, TensorOpera AI (https://TensorOpera.ai) is your generative AI platform at scale.
Track Federated Learning Papers
We propose dsDid, a federated learning package implemented in DataSHIELD with a federated version of the DID approach of Callaway and Sant'Anna (2022) at its core. It allows for the federated estimation of treatment effects per period and the corresponding federated uncertainty quantification.
This project implements the partially homomorphic Paillier algorithm and extends and optimizes encryption, decryption, matrix multiplication, and other operations on Numpy matrices, thereby enhancing speed.
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