The rapid expansion of the Internet of Medical Things (IoMT) has enhanced healthcare
services, but it has also exposed these systems to a growing range of cyberattacks. Addressing
this challenge requires effective intrusion detection solutions. Machine learning-based
intrusion detection systems (IDSs) offer a promising approach. In this research, we leverage
IoMT-TrafficData, a recently published dataset specific to IoMT environments, to develop
intrusion detection models based on federated learning (FL), which preserves data privacy
while enabling the use of lightweight learning algorithms suited to resource-constrained
devices. In addition, SHAP-based interpretability methods are employed to explain model
decisions, thereby improving reliability and transparency. Compared with existing IDS
solutions for IoMT, this research proposes an approach that balances performance, privacy,
and interpretability while accounting for resource limitations, resulting in three unified,
lightweight, and interpretable detection models tailored to different data types in IoMT
environments. This work thus provides a solid foundation for future research on secure
medical IoT systems.

Title

AI

Publisher

MDPI

Publisher Country

Switzerland

Indexing

Thomson Reuters

Impact Factor

6.5

Publication Type

Both (Printed and Online)

Volume

7

Year

2026

Pages

29