Revolutionizing Healthcare Harnessing IoT-Integrated Federated Learning for Early Disease Detection and Patient Privacy Preservation

Abstract

In an era where healthcare is increasingly reliant on technology, this chapter explores the transformative potential of IoT-integrated federated learning for optimizing healthcare privacy and detection. Leveraging IoT devices, this chapter presents a compelling approach to early disease detection, personalized treatment planning, remote patient monitoring, and clinical trial enhancement. Focusing on the pressing issue of early cancer detection, this chapter demonstrates how wearable and home IoT devices gather health data without compromising patient privacy. Federated learning models, decentralized and secure, process this data to identify health anomalies before symptoms manifest. Despite technical, regulatory, and social challenges, the benefits of heightened privacy, increased efficiency, and reduced healthcare costs drive the integration of IoT and federated learning, marking a profound advancement in the healthcare landscape. This chapter illuminates the path to a data-driven, patient-centric future.

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