@inproceedings{f35b288e895945c8966a71bdea8ea4da,
title = "APPFLx: Providing Privacy-Preserving Cross-Silo Federated Learning as a Service",
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",
keywords = "AI for science, Federated learning, HPC, IAM, federation as a service, privacy preserving, science as a service",
author = "Zilinghan Li and Shilan He and Pranshu Chaturvedi and Hoang, {Trung Hieu} and Minseok Ryu and Huerta, {E. A.} and Volodymyr Kindratenko and Jordan Fuhrman and Maryellen Giger and Ryan Chard and Kibaek Kim and Ravi Madduri",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 19th IEEE International Conference on e-Science, e-Science 2023 ; Conference date: 09-10-2023 Through 14-10-2023",
year = "2023",
doi = "10.1109/e-Science58273.2023.10254842",
language = "English (US)",
series = "Proceedings 2023 IEEE 19th International Conference on e-Science, e-Science 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "Proceedings 2023 IEEE 19th International Conference on e-Science, e-Science 2023",
address = "United States",
}