Federated Machine Learning for IoT/IoMT Security and Network Intelligence in HIPAA-Regulated Environments

Abstract

The increase in the deployment of Internet-of-Things (IoT) and Internet-of-Medical-Things (IoMT) devices in healthcare systems has posed unprecedented cybersecurity challenges that compromise patient safety and data integrity, on the one hand, but introduce demanding regulatory compliance provisions under HIPAA systems, on the other hand. Conventional centralized machine learning methods of network security are not sufficient in healthcare settings where privacy laws do not allow aggregation of uncoded patient data, device logs, and network traffic patterns in centralized repositories. The new concept called federated learning represents a groundbreaking solution that promotes the idea of collaborative intelligence between the distributed nodes without the need to place raw data in a centralized place. The suggested architecture coordinates distributed device fingerprinting, real-time anomaly detection, and behavioral analytics among heterogeneous network nodes, which include medical devices, clinical equipment, and enterprise IoT endpoints, by local model training and encrypted parameter aggregation. Privacy-preserving solutions such as differential privacy, homomorphic encryption, and secure multi-party computation are designed such that even updates of a model cannot be deanonymised to reveal sensitive information whilst maintaining the same detection ability as in centralized methods. Experimental analyses reveal that federated intelligence can significantly improve the detection of advanced multi-stage attacks and low-frequency anomalies by combining patterns that can be seen across many institutions and achieve high accuracy in device fingerprinting and anomaly detection with a low false positive rate, which would be appropriate in a clinical setting. The framework has been able to strike the right balance between the necessity to ensure security and the need to provide privacy, which allows healthcare institutions to combine their efforts to protect against changing cyber threats without violating data sovereignty and regulatory requirements. Application in a wide range of healthcare settings confirms that federated principles hold strong performance in the context of inherent heterogeneity of devices in terms of population, distribution, and computational resources, and can offer a technically viable route to improved network intelligence in controlled healthcare environments.