The Internet of Medical Things (IoMT) is revolutionizing healthcare by enabling continuous monitoring and real-time data collection through interconnected medical devices such as wearable sensors and smart health monitors. These devices generate sensitive physiological data, including cardiac signals, glucose levels, and vital signs, that are integrated into electronic health records (EHRs). Machine Learning (ML) and Deep Learning (DL) techniques have shown significant potential for predictive diagnostics and decision support based on such data. However, traditional centralized ML approaches raise significant privacy concerns due to the transmission and aggregation of sensitive health information. Additionally, compliance with data protection regulations, such as the Health Insurance Portability and Accountability Act (HIPAA) and General Data Protection Regulation (GDPR), restricts centralized data sharing and analytics. To address these challenges, this study introduces FED-EHR, a privacy-preserving Federated Learning (FL) framework that enables collaborative model training on distributed EHR datasets without transferring raw data from its source. The framework is implemented using Logistic Regression (LR) and Multi-Layer Perceptron (MLP) models and was evaluated using two publicly available clinical datasets: the UCI Breast Cancer Wisconsin (Diagnostic) dataset and the Pima Indians Diabetes dataset. The experimental results demonstrate that FED-EHR achieves a classification performance comparable to centralized learning, with ROC-AUC scores of 0.83 for the Diabetes dataset and 0.98 for the Breast Cancer dataset using MLP while preserving data privacy by ensuring data locality. These findings highlight the practical feasibility and effectiveness of applying the proposed FL approach in real-world IoMT scenarios, offering a secure, scalable, and regulation-compliant solution for intelligent healthcare analytics.

The Internet of Things (IoT) is a rapidly expanding network of interconnected things that use embedded sensors to gather and share data in real-time. IoT technologies have given rise to many networking applications in our everyday life such as smart homes, smart cities, smart transport, etc. Smart healthcare is one such application that has been revolutionized by the IoT, introducing a new branch of IoT known as the Internet of Medical Things (IoMT). IoMT encompasses an entire ecosystem consisting of smart wearable, implantable sensing equipment’s or devices, transmitters that are critical for monitoring the patients remotely and continuing the real-time and has opened the door to new innovative smart healthcare approaches while improving patient care outcomes. IoMT wearable and embedded sensing devices are commonly utilized in smart healthcare to capture medical data and transmit the medical data in a communication network stored in the cloud. The large volume of data generated and transmitted by these IoMT devices is rising at an exponential rate, resulting in an increase in security and privacy vulnerabilities of healthcare data. In order to ensure the Confidentiality and integrity of the IoMT devices and the Medical data, there should be proper security and privacy measures such as access control, passwords, multifactor authentication, and encryption of data generated, transmitted, or processed in the IoMT framework. In this paper, we identified the internet of things and its applications in smart healthcare systems. Additionally, the paper focuses on the architecture of IoMT, and several challenges, including the IoMT security and privacy requirements, and attack taxonomy. Finally, the review focuses on current security and privacy-enhancing techniques for IoMT or healthcare systems. The conclusion and future work are then discussed.