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This paper introduces XFL, an industrial-grade federated learning project.XFL supports training AI models collaboratively on multiple devices, whileutilizes homomorphic encryption, differential privacy, secure multi-partycomputation and other security technologies ensuring no leakage of data. XFLprovides an abundant algorithms library, integrating a large number ofpre-built, secure and outstanding federated learning algorithms, covering boththe horizontally and vertically federated learning scenarios. Numericalexperiments have shown the prominent performace of these algorithms. XFL buildsa concise configuration interfaces with presettings for all federationalgorithms, and supports the rapid deployment via docker containers.Therefore,we believe XFL is the most user-friendly and easy-to-develop federated learningframework. XFL is open-sourced, and both the code and documents are availableat https://github.com/paritybit-ai/XFL.

XFL: A High Performace, Lightweighted Federated Learning Framework