Federated Learning for Multi-Centre Medical Imaging

Authors

  • Douglas Porter Author

Keywords:

federated learning, medical imaging, privacy, multicentre validation, clinical AI

Abstract

This review examines federated learning for multi-centre medical imaging. The organizing question is when distributed training improves transportability while preserving privacy and institutional control. Ten related scholarly sources are synthesized through a decision-centered framework spanning problem definition, mechanism, measurement, evaluation, implementation, and governance. The review does not invent experiments, pooled estimates, or unreported quantitative results. It instead evaluates the strength and transferability of the available evidence, with particular attention to claiming privacy from data locality without testing leakage and governance failures. The resulting framework links technical or empirical performance to explicit use conditions and identifies tests that should precede wider adoption in collaborative imaging research and clinical model development.

References

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Published

2026-08-08

Issue

Vol. 1 (2026): Archive

Section

Review

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Copyright (c) 2026 Translational Medicine and Digital Health

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This work is licensed under a Creative Commons Attribution 4.0 International License.