Federated Learning (FL) Frameworks
- 01. FATE is an open-source project initiated by Webank’s AI Department to provide a secure computing framework to support the federated AI ecosystem.
- 02. Substra is an open source federated learning (FL) software. It enables the training and validation of machine learning models on distributed datasets.
- 03. PySyft is an open-source Python 3 based library that enables federated learning for research purposes and uses FL, differential privacy, and encrypted computations
- 04. TensorFlow Federated (TFF) is an open-source framework for machine learning and other computations on decentralized data.
- 05. IBM Federated Learning is a Python framework for federated learning (FL) in an enterprise environment.
- 06. NVIDIA FLARE (NVIDIA Federated Learning Application Runtime Environment) is a domain-agnostic, open-source, and extensible SDK for Federated Learning.
- 07. Flower - A Friendly Federated Learning Framework - A unified approach to federated learning, analytics, and evaluation. Federate any workload, any ML framework, and any programming language.
- 08. Fed-BioMed - An open-source federated learning framework.
- 09. XayNet is Xayn’s federated learning framework so that developers, companies and organisations can also train AI models directly on device and browser level
- 10. FLUTE (Federated Learning Utilities for Testing and Experimentation) - A platform for conducting high-performance federated learning simulations.
- 11. FedLab provides the necessary modules for FL simulation, including communication, compression, model optimization, data partition and other functional modules.
- 12. Open Federated Learning (OpenFL) is a Python 3 library for federated learning that enables organizations to collaboratively train a model without sharing sensitive information.