# XAIN Federated Learning

XAIN provides privacy-preserving technology dedicated to keep the data
used for the training of AI projects private. Our privacy engine for
machine learning is compliant with data privacy regulations such as
GDPR and CCPA. It offers a simple and scalable multi-party computation
based on federated learning that reflects technical and regulatory
needs of commercial AI projects.

## Resources

- [Whitepaper](https://www.xain.io/federated-learning-technology)
- [Source code](https://github.com/xainag/xain-fl/)
- [Rest API reference](https://xain-fl.readthedocs.io/en/latest/api)
- [Docker images](https://hub.docker.com/r/xain/xain-fl/)
- The platform backend is written in Rust. The `xain-fl` crate is [published on crates.io](https://crates.io/crates/xain-fl) and documentation is available [on docs.rs](https://docs.rs/xain-fl/0.7.0/xain_fl/)
- We provide a Python SDK to write Federated Learning participants in Python. The package can be found [is published on pypi.org](https://pypi.org/project/xain-sdk/) and the documentation [is hosted on readthedocs.io](https://xain-fl.readthedocs.io/projects/xain-sdk/en/latest/)