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.

翻译：联邦学习的实际部署是一项复杂工程，需要在数据科学、软件编程、网络通信与安全等不同领域的交叉地带开展研发行动。尽管目前已有多个联邦学习库面向数据科学家和用户推出，但由于医疗数据与医院基础设施的特殊挑战与需求，这些框架大多未能在医疗用例中找到无缝应用。此外，这些框架的治理机制、设计原则及安全假设通常缺乏清晰阐述，从而阻碍了其在敏感应用场景中的采纳。基于当前医疗领域联邦学习技术现状，本文介绍Fed-BioMed——一项致力于将联邦学习转化为真实世界医学研究应用的研究与开发计划。我们阐述了设计空间、目标用户、领域约束条件，以及这些因素如何影响当前与未来的软件架构。