Federated Learning for Privacy-Preserving Personalized Cardiovascular Monitoring with Wearable Sensors
Keywords:
federated learning; wearable sensors; cardiovascular monitoring; privacy preservation; personalization; edge computing; health data governanceAbstract
Wearable sensors now support continuous cardiovascular monitoring at population scale, providing longitudinal photoplethysmographic and electrocardiographic data that can inform early detection of arrhythmia, hypertension-related instability, and other cardiovascular conditions. However, centralized machine learning approaches require transferring sensitive physiological data to cloud environments, creating privacy, security, governance, and scalability problems. Federated learning offers a system-level alternative in which model updates are computed on distributed devices and only aggregated parameters are shared. This paper examines the architectural, infrastructural, and socio-technical dimensions of federated learning for privacy-preserving personalized cardiovascular monitoring with wearable sensors. It analyzes data quality challenges arising from motion artifacts and sensor heterogeneity, the design of secure aggregation and personalization mechanisms, and the trade-offs among privacy, utility, fairness, and energy consumption. The discussion further addresses robustness, clinical safety, regulatory compliance, and lifecycle management. The paper argues that federated cardiovascular monitoring should be understood not merely as a machine learning method but as a distributed health information infrastructure requiring coordinated governance, adaptive signal processing, equitable cohort representation, and sustainable edge operation. It concludes by identifying future directions for cross-institutional validation, energy-aware personalization, and regulatory alignment.
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