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

Zilinghan Li, Shilan He, Pranshu Chaturvedi, Trung-Hieu Hoang, Minseok Ryu +7 more
8/17/2023
cs.LG

No official code found. Best implementation: APPFL/APPFL (186 stars, MIT, last commit 2026-04-28).

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

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Code Implementations(1)

APPFL/APPFLCommunity76%
MIT

Advanced Privacy-Preserving Federated Learning framework

18642Shell, PythonOct 7, 20215 months agoMIT
federated-learning-frameworkprivacy-preserving-machine-learning

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Cite this paper

@article{li2023appflx,
  title  = {APPFLx: Providing Privacy-Preserving Cross-Silo Federated Learning as a Service},
  author = {Zilinghan Li and Shilan He and Pranshu Chaturvedi and Trung-Hieu Hoang and Minseok Ryu and E. A. Huerta and Volodymyr Kindratenko and Jordan Fuhrman and Maryellen Giger and Ryan Chard and Kibaek Kim and Ravi Madduri},
  year   = {2023},
  eprint = {2308.08786},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2308.08786v1}
}

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