학술논문
APPFLx: Providing Privacy-Preserving Cross-Silo Federated Learning as a Service
Document Type
Conference
Author
Source
2023 IEEE 19th International Conference on e-Science (e-Science) e-Science (e-Science), 2023 IEEE 19th International Conference on. :1-4 Oct, 2023
Subject
Language
ISSN
2325-3703
Abstract
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