What is the FATE Tool Assembly?
FATE (Federated AI Technology Enabler) is an open-source, industrial-grade federated learning framework. It supports secure computation protocols based on homomorphic encryption and multi-party computation (MPC), enabling collaborative machine learning across organizations without sharing raw data. FATE is modular, scalable, and backed by a robust community, making it suitable for large-scale, privacy-sensitive applications.
What can you use the FATE Tool Assembly for?
- Industrial federated learning in finance, healthcare, and other regulated sectors
- Secure multi-party computation and privacy-preserving analytics
- Large-scale, modular, and customizable federated learning deployments
- Integration with Kubernetes and cloud-native technologies
Limitations/Remarks
- Steep learning curve and deployment complexity
- High resource requirements for large-scale environments
- Ongoing development for vertical FL and deployment schemes
Specific Use Cases/Applications
- Privacy-preserving financial modeling
- Collaborative healthcare analytics
- Cross-organization machine learning with strict data privacy requirements
How to access the FATE Tool Assembly?
- Official Documentation
- GitHub Repository
- FedAI Community
- Install via Docker, Kubernetes, or PyPI
References
- FATE Documentation
- Liu, Y. et al. (2021). FATE: An Industrial Grade Platform for Collaborative Learning With Data Protection. JMLR. PDF
- FATE GitHub
Original Framework Contributors
- Yang Liu
- Tao Fan
- Tianjian Chen
- Qian Xu
- Qiang Yang
- FederatedAI Community
For a full list, see the FATE GitHub contributors.
More information
Training
Affiliations Contributors
Editor
KU Leuven / University of UHasselt