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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