A federated learning approach for smart healthcare systems

With periodic technology advancements and pandemic-like situations, remote patient health monitoring has increased significantly. The Internet of Things (IoT) devices, including wearables, sensors, and actuators deployed on the human body, detect and regulate physiological data. These systems can establish a trigger mechanism in the event of a possible health incident. Health monitoring using IoT devices generates a large amount of data. Several Machine Learning (ML) strategies have been utilized to analyze the collected data and derive precise predictions. The confidentiality of patient data is one of the essential requirements of these systems. It has been discovered that malicious coordination of ML algorithms might result in a massive attack surface, providing cyber- criminals with an accessible platform. Considering these requirements for using IoT systems for health monitoring, Federated Learning (FL) can solve data privacy challenges by training ML models locally on IoT de- vices without any data transfer to the cloud. FL facilitates information sharing among all IoT devices installed in hospitals through collabora- tive ML model training. This article will examine the significance of em- bedding FL into IoT-enabled smart hospitals and future guidelines for accomplishing this. This article discusses identifying rare diseases and critical care, resolving the problem of insufficient patient health data to train ML models, and preserving patient privacy.