Fed-BioMed: Open, Transparent and Trusted Federated Learning for Real-world Healthcare Applications

• URL: http://arxiv.org/abs/2304.12012v1
• Date: Mon, 24 Apr 2023 11:26:54 GMT
• Title: Fed-BioMed: Open, Transparent and Trusted Federated Learning for Real-world Healthcare Applications
• Authors: Francesco Cremonesi, Marc Vesin, Sergen Cansiz, Yannick Bouillard, Irene Balelli, Lucia Innocenti, Santiago Silva, Samy-Safwan Ayed, Riccardo Taiello, Laetita Kameni, Richard Vidal, Fanny Orlhac, Christophe Nioche, Nathan Lapel, Bastien Houis, Romain Modzelewski, Olivier Humbert, Melek \"Onen, and Marco Lorenzi
• Abstract summary: Fed-BioMed aims at translating federated learning into real-world medical research applications. We describe our design space, targeted users, domain constraints, and how these factors affect our current and future software architecture.
• Score: 4.086864536569863
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The real-world implementation of federated learning is complex and requires research and development actions at the crossroad between different domains ranging from data science, to software programming, networking, and security. While today several FL libraries are proposed to data scientists and users, most of these frameworks are not designed to find seamless application in medical use-cases, due to the specific challenges and requirements of working with medical data and hospital infrastructures. Moreover, governance, design principles, and security assumptions of these frameworks are generally not clearly illustrated, thus preventing the adoption in sensitive applications. Motivated by the current technological landscape of FL in healthcare, in this document we present Fed-BioMed: a research and development initiative aiming at translating federated learning (FL) into real-world medical research applications. We describe our design space, targeted users, domain constraints, and how these factors affect our current and future software architecture.

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