XFL: A High Performace, Lightweighted Federated Learning Framework

2023/02/10 by Hong Wang, Wang, Hong, Chi Zhang +10 · 1 citation
Computer Science · #FOS: Computer and information sciences #Internet Traffic Analysis and Secure E-voting #Machine Learning (cs.LG) #Privacy-Preserving Technologies in Data #Stochastic Gradient Optimization Techniques

paper · pdf · doi:10.48550/arxiv.2302.05076

openalex publication_date 2023/02/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Cited by

• On the Security and Privacy of Federated Learning: A Survey with Attacks, Defenses, Frameworks, Applications, and Future Directions

Related

• Advances in APPFL: A Comprehensive and Extensible Federated Learning Framework
• FLEX: FLEXible Federated Learning Framework
• Federated Machine Learning: Concept and Applications
• Decentral and Incentivized Federated Learning Frameworks: A Systematic Literature Review
• Optimization Methods and Software for Federated Learning