APPFLx: Providing Privacy-Preserving Cross-Silo Federated Learning as a Service
This work addresses the problem of simplifying and accelerating the adoption of privacy-preserving federated learning for domain experts and ML practitioners, though it is incremental as it builds on existing FL methods.
The paper tackles the challenge of making privacy-preserving cross-silo federated learning (PPFL) more accessible by introducing APPFLx, a ready-to-use platform that provides PPFL as a service, enabling users to easily orchestrate and evaluate FL experiments without sharing sensitive data.
Cross-silo privacy-preserving federated learning (PPFL) is a powerful tool to collaboratively train robust and generalized machine learning (ML) models without sharing sensitive (e.g., healthcare of financial) local data. To ease and accelerate the adoption of PPFL, we introduce APPFLx, a ready-to-use platform that provides privacy-preserving cross-silo federated learning as a service. APPFLx employs Globus authentication to allow users to easily and securely invite trustworthy collaborators for PPFL, implements several synchronous and asynchronous FL algorithms, streamlines the FL experiment launch process, and enables tracking and visualizing the life cycle of FL experiments, allowing domain experts and ML practitioners to easily orchestrate and evaluate cross-silo FL under one platform. APPFLx is available online at https://appflx.link