Federated learning for anomaly detection on Internet of Medical Things: A survey

The Internet of Medical Things (IoMT), an extension of the Internet of Things (IoT) paradigm where interconnected medical devices can sense and act within healthcare environments, aims to improve patient comfort, optimize outcomes and streamline medical processes. IoMT has seen significant growth in recent years, transforming healthcare with advanced monitoring, diagnostics, and data-sharing capabilities, though it also faces security and privacy challenges. The widespread attack surface of IoMT, combined with the difficulty of embedding robust security mechanisms in resource-constrained medical devices, makes IoMT systems particularly attractive targets for cyberattacks and a source of numerous security challenges. Anomaly detection systems are frequently part of the solution for IoMT cybersecurity, but they face unique integration challenges, especially in environments where patient data privacy is paramount. Federated Learning (FL) offers a promising approach to address these privacy concerns by enabling distributed training without sharing raw data. This paper provides a comprehensive literature review of FL applications in anomaly detection within IoMT ecosystems. It describes recent implementations, highlights the main open issues, and identifies future research challenges. This work elucidates the feasibility and challenges of FL-based anomaly detection systems applied to IoMT, offering insights for advancing IoMT security.