Back to Search Start Over

XFL: A High Performace, Lightweighted Federated Learning Framework

Authors :
Wang, Hong
Zhou, Yuanzhi
Zhang, Chi
Peng, Chen
Huang, Mingxia
Liu, Yi
Zhang, Lintao
Publication Year :
2023

Abstract

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.

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2302.05076
Document Type :
Working Paper