FedSmartCare: Design and Implementation of a Federated Learning Enabled Vital-Sign Monitoring System

The Medical Internet of Things (MIoT) is revolutionizing healthcare through continuous and real-time monitoring of vital signs, significantly benefiting applications ranging from general wellness and chronic disease management to elderly care. Integrating artificial intelligence (AI) into vital sign analysis has further advanced disease detection and predictive healthcare capabilities. However, conventional AI approaches often require transmitting sensitive health data collected by MIoT devices to centralized servers for model training and analysis, which raises substantial privacy and security concerns. To address these challenges, this study introduces FedSmartCare, a modular MIoT platform integrated with Federated learning (FL) designed explicitly for real-time, privacy-preserving monitoring and management of vital signs. By employing FL paradigms, FedSmartCare facilitates collaborative, decentralized AI model training at edge devices, significantly reducing privacy risks associated with centralized data handling. The proposed system’s performance is comprehensively validated through rigorous experiments on real-world data collected via FedSmartCare, along with three publicly available datasets. Multiple advanced AI models are systematically evaluated to assess the platform’s accuracy, efficiency, and robustness for classification and forecasting tasks involving both homogeneous and heterogeneous FL settings. In forecasting tasks, LSTM obtained MAE values of approximately 8.2 BPM and 3.5 BPM under homogeneous clients across two distinct datasets, while GRU achieved MAE values of 9.5 BPM under heterogeneous clients after 20 training rounds. For stress classification, a ResNet-based model reached 99% accuracy in the homogeneous setting over the same training horizon. These results demonstrate that FedSmartCare offers strong analytical performance while preserving data privacy, highlighting its potential for secure and intelligent MIoT-based healthcare solutions.