Syft is an open-source framework developed by OpenMined that provides tools for privacy-preserving machine learning. It is designed to facilitate Federated Learning (FL), where data remains on the client devices and only the model updates are shared with the central server. Syft allows you to implement various privacy-preserving techniques such as differential privacy, secure multi-party computation (SMPC), and homomorphic encryption.
https://github.com/OpenMined/PySyft?tab=readme-ov-file
https://blog.openmined.org/tag/pysyft/
https://openmined.github.io/PySyft/
A unified approach to federated learning, analytics, and evaluation. Federate any workload, any ML framework, and any programming language.
https://flower.ai/
https://flower.ai/docs/framework/tutorial-series-get-started-with-flower-pytorch.html
https://github.com/innovation-cat/Awesome-Federated-Machine-Learning
https://github.com/chaoyanghe/Awesome-Federated-Learning?tab=readme-ov-file
NVIDIA FLARE™ (NVIDIA Federated Learning Application Runtime Environment) is a domain-agnostic, open-source, and extensible SDK for Federated Learning. It allows researchers and data scientists to adapt existing ML/DL workflow to a federated paradigm. It enables platform developers to build a secure, privacy-preserving offering for a distributed multi-party collaboration.
https://developer.nvidia.com/flare
https://github.com/NVIDIA/NVFlare
https://www.apheris.com/resources/blog/top-7-open-source-frameworks-for-federated-learning