  The Internet of Medical Things (IoMT) transcends traditional medical
boundaries, enabling a transition from reactive treatment to proactive
prevention. This innovative method revolutionizes healthcare by facilitating
early disease detection and tailored care, particularly in chronic disease
management, where IoMT automates treatments based on real-time health data
collection. Nonetheless, its benefits are countered by significant security
challenges that endanger the lives of its users due to the sensitivity and
value of the processed data, thereby attracting malicious interests. Moreover,
the utilization of wireless communication for data transmission exposes medical
data to interception and tampering by cybercriminals. Additionally, anomalies
may arise due to human errors, network interference, or hardware malfunctions.
In this context, anomaly detection based on Machine Learning (ML) is an
interesting solution, but it comes up against obstacles in terms of
explicability and protection of privacy. To address these challenges, a new
framework for Intrusion Detection Systems (IDS) is introduced, leveraging
Artificial Neural Networks (ANN) for intrusion detection while utilizing
Federated Learning (FL) for privacy preservation. Additionally, eXplainable
Artificial Intelligence (XAI) methods are incorporated to enhance model
explanation and interpretation. The efficacy of the proposed framework is
evaluated and compared with centralized approaches using multiple datasets
containing network and medical data, simulating various attack types impacting
the confidentiality, integrity, and availability of medical and physiological
data. The results obtained offer compelling evidence that the FL method
performs comparably to the centralized method, demonstrating high performance.
Additionally, it affords the dual advantage of safeguarding privacy and
providing model explanation.
