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The real-world implementation of federated learning is complex and requiresresearch and development actions at the crossroad between different domainsranging 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 inmedical use-cases, due to the specific challenges and requirements of workingwith medical data and hospital infrastructures. Moreover, governance, designprinciples, and security assumptions of these frameworks are generally notclearly illustrated, thus preventing the adoption in sensitive applications.Motivated by the current technological landscape of FL in healthcare, in thisdocument we present Fed-BioMed: a research and development initiative aiming attranslating federated learning (FL) into real-world medical researchapplications. We describe our design space, targeted users, domain constraints,and how these factors affect our current and future software architecture.

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