APPFLx: Providing Privacy-Preserving Cross-Silo Federated Learning as a Service

TL;DR

APPFLx is a comprehensive platform that simplifies the deployment, management, and evaluation of privacy-preserving cross-silo federated learning, facilitating secure collaboration among trusted parties.

Contribution

It introduces a ready-to-use platform with secure authentication, multiple FL algorithms, and experiment tracking to promote adoption of cross-silo PPFL.

Findings

Streamlines federated learning experiment setup

Supports both synchronous and asynchronous algorithms

Enables visualization of FL lifecycle

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