B1 Other refereed article (e.g., editorial, letter, comment) in a scientific journal
Federated learning’s uncomfortable truth: why human networks matter more than neural networks
Authors: Peltonen, Laura-Maria; Chomutare, Taridzo
Publisher: Oxford University Press (OUP)
Publication year: 2026
Journal: Journal of the American Medical Informatics Association
Article number: ocag047
ISSN: 1067-5027
eISSN: 1527-974X
DOI: https://doi.org/10.1093/jamia/ocag047
Publication's open availability at the time of reporting: Open Access
Publication channel's open availability : Partially Open Access publication channel
Web address : https://doi.org/10.1093/jamia/ocag047
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/523080445
Self-archived copy's licence: CC BY
Self-archived copy's version: Publisher`s PDF
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.
Downloadable publication This is an electronic reprint of the original article. |
Funding information in the publication:
This work draws on insights generated within the FederatedHealth project, funded by Nordic Innovation and the work was supported by the Research Council of Finland (grant no. 372505).