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.
@article{arxiv.2302.05076,
title = {XFL: A High Performace, Lightweighted Federated Learning Framework},
author = {Hong Wang and Yuanzhi Zhou and Chi Zhang and Chen Peng and Mingxia Huang and Yi Liu and Lintao Zhang},
journal= {arXiv preprint arXiv:2302.05076},
year = {2023}
}