Open-source alternatives to Harmonia

FATE is an open-source federated learning platform that enables multiple organizations to collaboratively train machine learning models without exposing raw data to any party. It provides a complete framework for private data collaboration, allowing participants to jointly compute on sensitive information while maintaining data privacy and security guarantees through secure multi-party computation protocols. The platform distinguishes itself through its comprehensive infrastructure management capabilities, supporting automated deployment of multi-party clusters using Ansible-driven provisioni

FedML is a distributed machine learning training library, federated learning framework, and GPU workload orchestrator. It provides the core system components necessary to execute large-scale model training and fine-tuning across multi-cloud, on-premise, and decentralized GPU clusters, while offering a dedicated engine for scalable model serving and an MLOps pipeline manager for end-to-end lifecycle management. The platform distinguishes itself by enabling privacy-preserving federated learning across decentralized edge devices and organizational silos, keeping raw data on local hardware. It al

The zip file contains the source code we used in this paper to test the accuracy of face forgery detection in the Hybrid-domain forgery dataset.

《DCFL: Non-IID Awareness Dataset Condensation Aided Federated Learning》的部分代码 因为整体内容太大了，所以有选择的挑选上传 github，内容有些乱，也需要自己去在整理与看，希望能有一些帮助

PySyft is a privacy-preserving machine learning framework and remote computation engine. It functions as a decentralized data analysis orchestrator that allows for the execution of data science workflows on remote servers without requiring the transfer of raw private data from the host device. The platform provides a secure collaboration environment where data owners manage permissions and authorize specific collaborators to run computations. It differentiates its workflow by utilizing mock data for local development and validation before submitting final analysis jobs to private remote serve

This repository contains the source code for the paper DYNAFED: Tackling Client Data Heterogeneity with Global Dynamics. Our paper is accepted by CVPR2023 and is available on arXiv: link.

This is the official implementation of paper One-Shot Collaborative Distribution Matching in ECAI 2024.

FedD3 is a federated learning framework designed to train models using decentralized dataset distillation, requiring only a single communication round. This repository contains four main modules:

An open-source framework for machine learning and other computations on decentralized data.

The official implementation of paper "Overcoming Data and Model heterogeneities in Decentralized Federated Learning via Synthetic Anchors" accepted at ICML 2024

The official implementation of paper "Federated Learning on Virtual Heterogeneous Data with Local-GLobal Dataset Distillation" accepted at TMLR 2024

This repository is the official PyTorch implementation of: