Title: Privacy-Preserving Federated IoT Architecture for Early Stroke Risk Prediction
Description:
This project introduces a privacy-preserving federated IoT framework for early stroke risk prediction. The system is designed for smart healthcare environments where patient-related data may be collected from IoT devices, wearable sensors, or distributed healthcare sources. Instead of centralizing sensitive patient information, the proposed framework trains models locally and shares protected model updates through a federated learning process. The project aims to support early stroke-risk monitoring while reducing privacy risks linked with centralized data collection. It combines federated learning, IoT-based health monitoring, and privacy-aware model training to support secure and scalable healthcare intelligence.