Federated Learning for Multi-Centre Medical Imaging

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.

1. Dang, T. K., Lan, X., Weng, J., & Feng, M. (2022). Federated Learning for Electronic Health Records. ACM Transactions on Intelligent Systems and Technology, 13(5), 1-17. https://doi.org/10.1145/3514500 DOI
2. Hernandez-Cruz, N., Saha, P., Sarker, M. M. K., & Noble, J. A. (2024). Review of Federated Learning and Machine Learning-Based Methods for Medical Image Analysis. Big Data and Cognitive Computing, 8(9), 99. https://doi.org/10.3390/bdcc8090099 DOI
3. Kaissis, G. A., Makowski, M. R., Rückert, D., & Braren, R. F. (2020). Secure, privacy-preserving and federated machine learning in medical imaging. Nature Machine Intelligence, 2(6), 305-311. https://doi.org/10.1038/s42256-020-0186-1 DOI
4. Linardos, A., Kushibar, K., Walsh, S., Gkontra, P., & Lekadir, K. (2022). Federated learning for multi-center imaging diagnostics: a simulation study in cardiovascular disease. Scientific Reports, 12(1), 3551. https://doi.org/10.1038/s41598-022-07186-4 DOI
5. Mouhni, N., Elkalay, A., Chakraoui, M., Abdali, A., Ammoumou, A., & Amalou, I. (2022). Federated Learning for Medical Imaging: An Updated State of the Art. Ingénierie des systèmes d information, 27(1), 143-150. https://doi.org/10.18280/isi.270117 DOI
6. Nazir, S., & Kaleem, M. (2023). Federated Learning for Medical Image Analysis with Deep Neural Networks. Diagnostics, 13(9), 1532. https://doi.org/10.3390/diagnostics13091532 DOI
7. Oh, W., & Nadkarni, G. N. (2023). Federated Learning in Health care Using Structured Medical Data. Advances in Kidney Disease and Health, 30(1), 4-16. https://doi.org/10.1053/j.akdh.2022.11.007 DOI
8. Rehman, M. H. U., Pinaya, W. H. L., Nachev, P., Teo, J. T., Ourselin, S., & Cardoso, M. J. (2023). Federated learning for medical imaging radiology. British Journal of Radiology, 96(1150), 20220890. https://doi.org/10.1259/bjr.20220890 DOI
9. Sandhu, S. S., Gorji, H. T., Tavakolian, P., Tavakolian, K., & Akhbardeh, A. (2023). Medical Imaging Applications of Federated Learning. Diagnostics, 13(19), 3140. https://doi.org/10.3390/diagnostics13193140 DOI
10. Wang, J., Jin, Y., Stoyanov, D., & Wang, L. (2023). FedDP: Dual Personalization in Federated Medical Image Segmentation. IEEE Transactions on Medical Imaging, 43(1), 297-308. https://doi.org/10.1109/tmi.2023.3299206 DOI