Federated learning framework made by researchers for researchers :)

Perform data science on data that remains in someone else's server

Simulation framework for accelerating research in Private Federated Learning

🎓 Automatically Update Some Fields Papers Daily using Github Actions (Update Every 12th hours)

Track Federated Learning Papers

Flower: A Friendly Federated Learning Framework

Federated data managment insights with Plotly Dash, Vantage6, and a GraphDB triplestore

This repository contains the hub packages & services of FLAME.

A Comprehensive Survey of Forgetting in Deep Learning Beyond Continual Learning. arXiv:2307.09218.

Decentralized & federated privacy-preserving ML training, using p2p networking, in JS

Implemented the FedAvg Algorithm in Federated Learning. Completed its uncertainty estimation and confidence calibration.

AI/ ML papers in DBLP/ arXiv

A curated list of advancements in Vertical Federated Learning, frameworks and libraries.

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

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

Federated Learning Algorithm FedAvg implementation in PyTorch

The project focuses on utilizing federated machine learning to enhance the detection of malware in Internet of Things (IoT) devices. The code includes experiments simulating various configurations where clients collaboratively train deep learning models for malware detection without sharing raw data.

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.