A Secure and Privacy-Preserving Paradism Based on Blockchain and Federated Learning for CIoMT in Smart Healthcare Systems

Since the advent of COVID-19 pandemic, the Cognitive Internet of Medical Things (CIoMT) has been highlighted as a critical need for the healthcare ecosystem, by enhancing operational efficiency and promoting preventive and proactive healthcare approaches through a remote patient monitoring, real-time health data collection and optimized supply chain management. Indeed, the CIoMT is a promising technology that refers to the application of cognitive computing techniques and the Internet of Things (IoT) in the field of e-health to enhance the delivery of healthcare services. However, challenges emerged in data privacy, service integrity, and adaptability to network structure in a such ecosystem since health data are highly private and have great financial values. To deal with these concerns, we propose in this paper a secure and trusted infrastructure based on Federated Learning and Blockchain technologies within a Fog Computing network. The adopted technologies have the potentials to overcome the issue of fragmented data repositories, by providing a distributed model for health data sharing while preserving the privacy of data owners within a trusted collaborative environment based on an Identity Federation paradigm.