APPFLx: Providing Privacy-Preserving Cross-Silo Federated Learning as a
  Service
Zilinghan Li Shilan He Pranshu Chaturvedi Trung-Hieu Hoang Minseok Ryu E. A. Huerta Volodymyr Kindratenko Jordan Fuhrman Maryellen Giger Ryan Chard Kibaek Kim Ravi Madduri
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