Privacy-Preserving Federated IoT Architecture for Early Stroke Risk Prediction

Abstract

Stroke is one of the leading causes of death and long-term disability worldwide, and effective prevention depends on fast, reliable, and privacy-preserving risk assessment. This study proposes a federated IoT-enabled framework that combines feature-optimized machine learning (ML) with real-time patient monitoring to predict and detect brain stroke risk. The system operates in two stages: (i) a stroke prediction module that builds an ML model for risk assessment and (ii) an IoT-based framework that continuously monitors patients and triggers timely alerts. The ML pipeline starts from a clinical–physiological dataset containing 17 initial attributes and applies a feature optimization strategy based on feature importance, selection, and reduction to identify the most informative predictors of stroke. To support multi-center deployment while protecting patient confidentiality, the ML pipeline is embedded within a standard Federated Averaging (FedAvg) architecture, where multiple home or hospital IoT gateways collaboratively train a shared global model without exchanging raw patient data. In each communication round, clients perform local training and the server aggregates client model parameters to update the global model. The resulting federated global model matches the performance of the centralized baseline, achieving 99.44% test accuracy while preserving data locality. Integrated with IoT devices, the system can detect pre-stroke syndromes in real time and automatically notify family members or emergency medical services, making it suitable for both home and hospital environments and offering a practical path toward early intervention and improved stroke outcomes.