Confidential Federated Learning Framework for Predictive Healthcare Analytics Using IoT

Abstract

The rapid growth of the Internet of Things (IoT) in healthcare has enabled continuous acquisition of physiological and clinical information through wearable sensors and connected medical devices, creating new opportunities for predictive healthcare analytics. However, the centralized collection and processing of such sensitive information raises significant concerns regarding patient privacy, data security and institutional data ownership. This study proposes a confidential federated learning framework for IoT-enabled cardiovascular risk prediction using secondary healthcare data. A publicly available cardiovascular disease dataset is preprocessed and distributed among multiple virtual healthcare clients to simulate heterogeneous healthcare institutions. Each client independently trains a neural-network-based cardiovascular risk prediction model using locally retained data, while only model updates are communicated to the federated server. To strengthen confidentiality, gradient clipping and differential-privacy noise are incorporated before model updates are transmitted for federated aggregation. The global model is generated using weighted Federated Averaging and iteratively redistributed to participating clients. The framework is intended to demonstrate that collaborative cardiovascular-risk prediction can be achieved without direct exchange of raw patient information while maintaining an appropriate balance between predictive performance, confidentiality and communication efficiency. The proposed approach provides a computational foundation for privacy-preserving predictive healthcare analytics in distributed IoT environments.