NAACL '24 (Demo) / MlSys @ NeurIPS '23 - RedCoast: A Lightweight Tool to Automate Distributed Training and Inference

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

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

NVIDIA Federated Learning Application Runtime Environment

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.

This repo contains the code and experiments of the paper "User-Empowered Federated Learning in Android"

Clustered Multi-layer Federated Learning with Rough Estimators.

Flower: A Friendly Federated Learning Framework

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

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

This repository contains the hub packages & services of FLAME.

Federated learning framework made by researchers for researchers :)

Paper & Code & Dataset Collection of Spatial-Temporal Data Mining.

FedAnil+ is a novel lightweight, and secure Federated Deep Learning Model to address non-IID data, privacy concerns, and communication overhead. This repo hosts a simulation for FedAnil+ written in Python.

FedDebug is a novel testing and debugging technique that localizes faulty clients in Federated Learning.

Simulation framework for accelerating research in Private Federated Learning

An Industrial Grade Federated Learning Framework

An Git-like version control file system for data lineage & data collaboration.

A repo that takes you through some principles about data privacy based on the Kenya Data Protection Act and General Data Protection Regulation. Useful for a data person.

PyTorch implementation of Federated Learning with Reduced Information Leakage and Computation