XFL: A High Performace, Lightweighted Federated Learning Framework
This framework addresses the need for secure and user-friendly federated learning tools for developers and industries, but it appears incremental as it builds on existing technologies.
The paper introduces XFL, an industrial-grade federated learning framework that supports collaborative model training across multiple devices with security features like homomorphic encryption and differential privacy, and it demonstrates prominent performance in numerical experiments.
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.