Recent Trends in Electronics Communication Systems Review Article
Federated Intelligence for Privacy-Preserving Health Data Analytics
Abstract
Healthcare artificial intelligence is limited by a structural conflict: useful models benefit from diverse multi-institutional data, while clinical records cannot be freely pooled because of privacy, ownership and governance constraints. This study presents a practical federated-learning framework for privacy-preserving health data analytics and validates its basic learning behavior in a reproducible cross-silo simulation. Five non-identical institutional clients were created from the public Breast Cancer Wisconsin (Diagnostic) dataset, and a standardized logistic classifier was trained using weighted Federated Averaging for 50 communication rounds without transferring raw training records between clients. The federated model achieved 98.25% test accuracy, 95.24% malignant-case recall, 97.56% F1-score and 0.9983 ROC-AUC, closely matching an equivalent centralized control and exceeding the mean performance of independently trained single-site models. The paper emphasizes that federated learning reduces raw-data movement but does not by itself constitute a complete privacy guarantee. A defense-in-depth architecture is therefore proposed that combines local data control, encrypted communication, secure aggregation, optional differential privacy, model monitoring and clinical governance. Seven figures and five comparative tables summarize the system architecture, cross-site heterogeneity, convergence, predictive performance and operational safeguards. The resulting framework is intended as an accessible implementation model for hospitals, laboratories, imaging centers and connected-health platforms seeking collaborative AI analytics while retaining institutional control of sensitive health data.
Keywords
References (20)
- McMahan B, Moore E, Ramage D, Hampson S, y Arcas BA. Communication-efficient learning of deep networks from decentralized data. InArtificial intelligence and statistics 2017 Apr 10 (pp. 1273- 1282). Pmlr.
- Rieke N, Hancox J, Li W, Milletari F, Roth HR, Albarqouni S, et al. The future of digital health with federated learning. npj Digit Med. 2020;3:119. doi:10.1038/s41746-020-00323-1.
- Sheller MJ, Edwards B, Reina GA, Martin J, Pati S, Kotrotsou A, et al. Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data. Sci Rep. 2020;10:12598. doi:10.1038/s41598-020-69250-1.
- Kaissis GA, Makowski MR, Rückert D, Braren RF. Secure, privacy-preserving and federated machine learning in medical imaging. Nature Machine Intelligence. 2020;2:305–311. doi:10.1038/s42256-020-0186-1.
- Brisimi TS, Chen R, Mela T, Olshevsky A, Paschalidis IC, Shi W. Federated learning of predictive models from federated electronic health records. International Journal of Medical Informatics. 2018;112:59–67. doi:10.1016/j.ijmedinf.2018.01.007.
- Dayan I, Roth HR, Zhong A, Harouni A, Gentili A, Abidin AZ, et al. Federated learning for predicting clinical outcomes in patients with COVID-19. Nature Medicine. 2021;27(10):1735–1743. doi:10.1038/s41591-021-01506-3.
- Warnat-Herresthal S, Schultze H, Shastry KL, Manamohan S, Mukherjee S, Garg V, et al. Swarm Learning for decentralized and confidential clinical machine learning. Nature. 2021;594:265–270. doi:10.1038/s41586-021-03583-3.
- Li T, Sahu AK, Zaheer M, Sanjabi M, Talwalkar A, Smith V. Federated Optimization in Heterogeneous Networks. Proceedings of Machine Learning and Systems. 2020;2:429–450.
- Kairouz P, McMahan HB, Avent B, Bellet A, Bennis M, Bhagoji AN, et al. Advances and open problems in federated learning. Foundations and Trends in Machine Learning. 2021;14(1–2):1–210. doi:10.1561/2200000083.
- Bonawitz K, Ivanov V, Kreuter B, Marcedone A, McMahan HB, Patel S, et al. Practical secure aggregation for privacy-preserving machine learning. In: Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security. 2017. p. 1175–1191. doi:10.1145/3133956.3133982.
- Abadi M, Chu A, Goodfellow I, McMahan HB, Mironov I, Talwar K, Zhang L. Deep learning with © STM Journals 2021. All Rights Reserved 3 differential privacy. In: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security. 2016. p. 308–318. doi:10.1145/2976749.2978318.
- Thakur A, Molaei S, Nganjimi PC, Liu F, Soltan A, Schwab P, et al. Knowledge abstraction and filtering based federated learning over heterogeneous data views in healthcare. npj Digital Medicine. 2024;7:283. doi:10.1038/s41746-024-01272-9.
- Peng L, Luo G, Zhou S, Chen J, Xu Z, Sun J, et al. An in-depth evaluation of federated learning on biomedical natural language processing for information extraction. npj Digital Medicine. 2024;7:127. doi:10.1038/s41746-024-01126-4.
- Li M, Xu P, Hu J, Tang Z, Yang G. From challenges and pitfalls to recommendations and opportunities: Implementing federated learning in healthcare. Medical Image Analysis. 2025;101:103497. doi:10.1016/j.media.2025.103497.
- Madathil NT, Dankar FK, Gergely M, Belkacem AN, Alrabaee S. Revolutionizing healthcare data analytics with federated learning: A comprehensive survey of applications, systems, and future directions. Computational and Structural Biotechnology Journal. 2025;28:217–238. doi:10.1016/j.csbj.2025.06.009.
- Eden R, Chukwudi I, Bain C, Barbieri S, Callaway L, de Jersey S, et al. A scoping review of the governance of federated learning in healthcare. npj Digital Medicine. 2025;8:427. doi:10.1038/s41746-025-01836-3.
- Mohammadi M, Vejdanihemmat M, Lotfinia M, Rusu M, Truhn D, Maier A, Tayebi Arasteh S. Differential privacy for medical deep learning: methods, tradeoffs, and deployment implications. npj Digital Medicine. 2026;9:93. doi:10.1038/s41746-025-02280-z.
- Kanauzia R, Singh M, Gulati S, Singh BM, Arora N, et al. A comprehensive survey on federated learning for privacy preservation in digital healthcare applications. Knowledge and Information Systems. 2026;68:55. doi:10.1007/s10115-025-02673-2.
- Wolberg W, Mangasarian O, Street N, Street W. Breast Cancer Wisconsin (Diagnostic) [dataset]. UCI Machine Learning Repository; 1993. doi:10.24432/C5DW2B.
- Linardos A, Kushibar K, Walsh S, Gkontra P, Lekadir K. Federated learning for multi-center imaging diagnostics: a simulation study in cardiovascular disease. Scientific Reports. 2022;12:3551. doi:10.1038/s41598-022-07186-4.