Federated learning’s uncomfortable truth: why human networks matter more than neural networks

OBJECTIVES: To examine real-world barriers to implementing federated learning in healthcare and highlight the organizational, regulatory, and socio-technical factors often overlooked in technical research. MATERIALS AND METHODS: Insights were derived from a 3-year implementation of a Nordic-Baltic federated health data network involving 5 countries and 9 institutions, incorporating legal, organizational, and cross-disciplinary perspectives. RESULTS: Structural challenges included coordination burdens, divergent interpretations of privacy and risk, epistemological gaps between disciplines, and the absence of legal frameworks for multi-country distributed learning in Europe. These constraints limited progress despite the availability of robust technical solutions. DISCUSSION: Technical privacy measures alone cannot replace trust-building, governance development, and cross-disciplinary translation work. Federated learning is more accurately understood as a socio-technical collaboration model rather than a purely technical architecture. CONCLUSION: Pre-implementation planning, tiered participation models, and strengthened governance are essential to support equitable, sustainable, and clinically impactful adoption of federated learning in healthcare.