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

Abstract

<p>Objectives</p><p>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.</p><p>Materials and Methods</p><p>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.</p><p>Results</p><p>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.</p><p>Discussion</p><p>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.</p><p>Conclusion</p><p>Pre-implementation planning, tiered participation models, and strengthened governance are essential to support equitable, sustainable, and clinically impactful adoption of federated learning in healthcare.</p>