Federated learning: a privacy-preserving approach to data-centric regulatory cooperation

Federated learning: a privacy-preserving approach to data-centric regulatory cooperation

Regulatory agencies aim to ensure the safety and efficacy of medical products but often face legal and privacy concerns that hinder collaboration at the data level. In this paper, we propose federated learning as an innovative method to enhance data-centric collaboration among regulatory agencies by...

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Bibliographic Details
Main Authors: Alexander Horst, Paul Loustalot, Sanjeev Yoganathan, Ting Li, Joshua Xu, Weida Tong, David Schneider, Nicolas Löffler-Perez, Erminio Di Renzo, Michael Renaudin
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-05-01
Series:Frontiers in Drug Safety and Regulation
Subjects:
federated learning
regulatory sciences
medical devices
risk assessment
swissmedic
data privacy
Online Access:https://www.frontiersin.org/articles/10.3389/fdsfr.2025.1579922/full
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https://www.frontiersin.org/articles/10.3389/fdsfr.2025.1579922/full

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