Advances in Anomaly Detection in Healthcare Using Federated Machine Learning

DOI

10.2991/978-94-6239-678-4_8How to use a DOI?

Keywords

Anomaly Detection; Cyber Security; Edge Intelligence; Federated Learning; Internet of Medical Things (IoMT) Healthcare; Privacy Preservation

Abstract

In the field of healthcare, anomaly detection is essential in identifying the abnormal tendencies of patient records, function and/or medical procedures of a device. Electronic health records (EHRs), wearable sensors, and Internet of Medical Things (IoMT) devices are big and have created certain challenges in ensuring data privacy and regulatory compliance. The traditional centralized systems of learning are limited by the data sharing restriction and the data security issues. The Federated Machine Learning (FML) eliminates all these drawbacks by allowing model training in the presence of distributed sources, yet without a direct exchange of patient data. In this review, FML-based systems of detection of anomalies in healthcare are thoroughly analyzed in terms of their architecture, mechanisms of privacy protection, and use scenarios. The uniqueness of the given work is in the combination of the experience of two spheres federated learning and healthcare anomaly detection, where privacy-sensitive, distributed analytics is seen as a single unit. Moreover, the main issues of heterogeneity of data, the efficiency of communication and security of the model are addressed with the prospects of the future research. The goal is to influence the creation of secure and scalable healthcare solutions that have the ability to identify anomalies in an efficient manner without compromising the privacy of data.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Shivangi Valand
AU  - Umesh Kumar
AU  - Yatin Shukla
PY  - 2026
DA  - 2026/05/28
TI  - Advances in Anomaly Detection in Healthcare Using Federated Machine Learning
BT  - Proceedings of the 2nd International Conference on Recent Advancement and Modernization in Sustainable Intelligent Technologies & Applications (RAMSITA-2026)
PB  - Atlantis Press
SP  - 90
EP  - 102
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-678-4_8
DO  - 10.2991/978-94-6239-678-4_8
ID  - Valand2026
ER  -