Federated Artificial Intelligence approaches for wearables and health devices

This review paper explores the integration of Federated Learning (FL) with wearable health devices, emphasizing its transformative potential in healthcare applications. The study systematically examines key criteria influencing the implementation and effectiveness of FL, particularly focusing on privacy data preservation, sensor technology, cloud computing, and blockchain integration. By comparing existing literature, our work highlights FL’s ability to enhance data security and privacy while enabling real-time health monitoring and personalized treatment plans. The analysis includes a comprehensive examination of technical frameworks, emphasizing the use of wearable sensors and IoT devices in remote patient monitoring and chronic disease management. Additionally, the review addresses the challenges of scalability, interoperability, and regulatory compliance, proposing innovative strategies to overcome these barriers. Through this effort, the paper contributes to the expanding research on decentralized healthcare solutions, offering insights into the future directions and practical implications of FL in wearable health technologies.