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




AuthorsPeltonen, Laura-Maria; Chomutare, Taridzo

PublisherOxford University Press (OUP)

Publication year2026

Journal: Journal of the American Medical Informatics Association

Article numberocag047

ISSN1067-5027

eISSN1527-974X

DOIhttps://doi.org/10.1093/jamia/ocag047

Publication's open availability at the time of reportingOpen 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 addresshttps://research.utu.fi/converis/portal/detail/Publication/523080445

Self-archived copy's licenceCC BY

Self-archived copy's versionPublisher`s PDF


Abstract

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.
This reprint may differ from the original in pagination and typographic detail. Please cite the original version.




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).


Last updated on 07/05/2026 02:38:16 PM