  This paper introduces XFL, an industrial-grade federated learning project.
XFL supports training AI models collaboratively on multiple devices, while
utilizes homomorphic encryption, differential privacy, secure multi-party
computation and other security technologies ensuring no leakage of data. XFL
provides an abundant algorithms library, integrating a large number of
pre-built, secure and outstanding federated learning algorithms, covering both
the horizontally and vertically federated learning scenarios. Numerical
experiments have shown the prominent performace of these algorithms. XFL builds
a concise configuration interfaces with presettings for all federation
algorithms, and supports the rapid deployment via docker containers.Therefore,
we believe XFL is the most user-friendly and easy-to-develop federated learning
framework. XFL is open-sourced, and both the code and documents are available
at https://github.com/paritybit-ai/XFL.
