Advancing Healthcare IoT: Blockchain and Federated Learning Integration for Enhanced Security and Insights

Abstract

Fast-growing IoT devices in healthcare have ushered in a new era of data-driven patient care and treatment. These gadgets capture too much sensitive medical data, a major issue. We need to boost data analysis, security, and privacy. This research uses cutting-edge concepts like federated learning and blockchain technology to improve Healthcare IoT analytics and security. This study examines blockchain and federated learning's numerous integration aspects. It begins with data security, employing blockchain's immutability and decentralization to prevent manipulation. Federation learning was utilized to solve privacy concerns while adhering to tight healthcare data security regulations, providing strong data analysis without releasing raw, patient-specific information. This study examines how federated learning and blockchain technologies enhance healthcare IoT systems' analytics. This connection allows safe, collaborative analysis among healthcare professionals and devices, offering more insights for research, diagnostics, and tailored patient care. How to integrate these cutting-edge technologies into healthcare IoT networks is also explored.