Federated Learning for Privacy-Preserving Wearable Health Monitoring
Keywords:
federated learning, wearable health monitoring, privacy preservation, edge computing, system architecture, health data governanceAbstract
Wearable health monitoring has transitioned from episodic clinical observation to continuous, high-resolution physiological data collection, creating new opportunities for early detection, chronic disease management, and personalized intervention. At the same time, the centralized aggregation of such sensitive data introduces substantial privacy, security, and governance risks. Federated learning has emerged as a system-level alternative that enables distributed model training without transferring raw physiological records to a central repository. This paper presents an interdisciplinary examination of federated learning for privacy-preserving wearable health monitoring, emphasizing structural trade-offs rather than algorithmic details. The analysis addresses the architecture of wearable sensing pipelines, the role of edge aggregation, the limits of federated learning as a privacy mechanism, and the complementary use of differential privacy and secure aggregation. It further considers data heterogeneity across devices, patient populations, and behavioral contexts, together with challenges related to robustness, fairness, personalization, and long-term sustainability. The discussion incorporates healthcare deployment experiences, edge computing constraints, and governance frameworks that shape clinical and consumer adoption. While federated learning can reduce the necessity of raw data sharing, it does not eliminate privacy risk, nor does it automatically ensure clinical validity or operational resilience. A layered systems perspective is therefore required, spanning sensing hardware, communication infrastructure, model coordination, institutional policy, and regulatory compliance. The paper concludes by identifying future directions for resilient, equitable, and sustainable federated health monitoring infrastructures.
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