Impact Of Federated Learning On Patient Healthcare Monitoring Model Approach

Abstract

The integration of wearable devices, IoT, and mobile internet technology has led to the development of smart healthcare, which enables dynamic access to information, interconnectivity among individuals, materials, and institutions, and intelligent management of medical demands. In the context of a medical center in smart cities, real-time patient monitoring is crucial for accurate treatment outcomes. Despite the availability of several patient monitoring systems, their performance has not been optimal due to the lack of real-time patient monitoring. To address this challenge, this research proposes the use of a Federated Learning-based smart patient monitoring system. Federated Learning is a cutting-edge machine learning technique that trains algorithms across multiple decentralized devices or servers, each holding local data samples, without the need for data exchange. The proposed approach seeks to provide effective real-time monitoring of patients' healthcare records, thereby improving the accuracy and efficiency of patient treatment. By harnessing the power of Federated Learning, this proposed system is expected to revolutionize the way patients are monitored and treated in smart healthcare centers, leading to better health outcomes.