Federated Learning and Blockchain-Enabled Privacy-Preserving Healthcare 5.0 System: A Comprehensive Approach to Fraud Prevention and Security in IoMT

The proliferation of Internet and Communication Technologies (ICTs) has ushered in a period often referred to as Industry 5.0. The subsequent development is accompanied by the healthcare industry coining Healthcare 5.0. Healthcare 5.0 incorporates the Internet of Things (IoT), enabling medical imaging technologies to facilitate early diagnosis of diseases and enhance the quality of healthcare facilities' service. Nevertheless, the healthcare sector is currently experiencing a delay in adopting Artificial Intelligence (AI) and big data technologies compared to other sectors under the umbrella of Industry 5.0. This delay may be attributed to the prevailing concerns about data privacy within the healthcare domain. In recent times, there has been a noticeable increase in the use of Machine Learning (ML) enabled adaptive Internet of Medical Things (IoMT) systems with different technologies for medical applications. ML is an essential component of the IoMT system, as it optimizes the trade-off between delay and energy consumption. The issue of data fraud in classical learning models inside the distributed IoMT system for medical applications remains a significant research challenge in practical settings. This paper proposes Federated Learning and Blockchain-Enabled Privacy-Preserving (FL-BEPP) for Fraud Prevention and Security (FPS) in the IoMT framework. The system incorporates numerous dynamic strategies. This research examines the medical applications that exhibit hard constraints, such as deadlines, and soft constraints, such as resource consumption, when executed on distributed fog and cloud nodes. The primary objective of FL-BEPP is to effectively detect and safeguard the confidentiality and integrity of data across many tiers, including local fog nodes and faraway clouds. This is achieved by minimizing power use and delay while simultaneously meeting the time constraints associated with healthcare workloads.