A roadmap for federated learning projects using health data to guide sustainable artificial intelligence development...

The rise of digital health data has expanded opportunities for data-driven innovation, yet privacy, legal and ethical barriers frame data sharing and collaborative artificial intelligence development. Federated Learning (FL) offers a privacy-preserving alternative, but current research considers mainly technical aspects. There is no end-to-end roadmap that integrates ethical, legal, technical and administrative principles tailored to FL projects in healthcare. This study addresses that gap by developing a roadmap to guide responsible and scalable FL research in the European context.

A multi-method participatory approach was used to develop a roadmap for scientific projects using FL on health data. The iterative process involved three phases. First, key questions were defined and existing evidence was explored through (i) a survey of domain experts (researchers, data governance specialists and infrastructure providers), (ii) a targeted literature review of FL applications in health research and (iii) systematic mapping of relevant EU-level legislation and policy frameworks. Evidence from these sources was synthesized to identify technical, organizational, legal and sustainability-related requirements for FL-based research. Second, preliminary roadmap components were refined through stakeholder engagement in an online workshop, where feasibility, scalability and sustainability considerations were explicitly discussed. Third, the roadmap was validated and iteratively refined by an expert panel through a structured group discussion, focusing on long-term sustainability, governance and transferability across research contexts. The process was carried out within a Baltic-Nordic collaboration in 2023-2025.

The developed roadmap integrates ethical, legal, technical, administrative and sustainability-related considerations essential for applying FL to health data. It emphasizes the importance of multidisciplinary collaboration throughout the FL project lifecycle, with particular attention to long-term governance, scalability and reuse of infrastructures and practices. The process is structured into six phases: (1) Planning, (2) Execution refinement, (3) Data, (4) FL platform, (5) FL experiment and (6) Dissemination. Across these phases, sustainability is addressed through mechanisms such as regulatory alignment, shared governance models, capacity building and integration with existing research and health data infrastructures. By merging ethical, legal, technical and administrative aspects into a unified, end-to-end framework, the roadmap provides actionable, novel guidance beyond existing recommendations.

This work consolidates early lessons from FL in healthcare into a practical, step-by-step roadmap that integrates ethical, legal, technical and administrative aspects in the European context. By offering a shared framework for diverse stakeholders, it supports more trustworthy, scalable and compliant AI collaborations across healthcare systems.