A Federated Learning Model for Detecting Cyberattacks in Internet of Medical Things Networks

The Internet of Medical Things (IoMT) has significantly enhanced the healthcare sector by enabling advanced connectivity between smart medical devices, improving patient monitoring, and optimizing care quality. However, this increasing connectivity exposes these systems to cyberattacks that can compromise data confidentiality, disrupt device operations and endanger patient safety. Attacks on the IoMT, such as intrusions, ransomware, data tampering and denial-of-service, exploit vulnerabilities in medical devices, which often lack adequate security measures. Addressing these challenges requires robust attack detection solutions tailored to the constraints of IoMT environments. In this paper, we propose a novel approach based on Federated Learning (FL), enabling medical devices to collaboratively detect cyberattacks without directly sharing sensitive data. Unlike traditional FL systems that rely on computationally intensive neural networks, our approach leverages XGBoost—a lightweight yet powerful algorithm—to train detection models locally on devices. The XGBoost models are further optimized using a Bayesian method and integrated with an aggregation algorithm to construct an adaptive global model. We evaluate our approach using three IoMT datasets: CICIoMT2024, ECU-IoHT, and WUSTL-EHMS. Experimental results demonstrate that, in addition to minimizing the risk of medical data leakage, our method achieves superior detection accuracy compared to centralized approaches.