Federated Generative Learning for Privacy-Preserving Personalized Health Monitoring with Wearable Sensors

Abstract

Wearable sensors generate continuous, context-rich physiological data that can support early detection, chronic disease management, and personalized health intervention. However, centralized collection of such data creates substantial privacy, security, and governance risks. Federated generative learning offers a system-level alternative in which distributed devices collaboratively train generative models without transferring raw sensor data. This paper examines federated generative learning for privacy-preserving personalized health monitoring from a socio-technical and infrastructural perspective. It analyzes architectural trade-offs among communication efficiency, personalization, generative fidelity, and privacy protection. The discussion addresses heterogeneity across devices and populations, secure aggregation, differential privacy, gradient leakage, and synthetic data memorization. It further considers deployment infrastructure, energy sustainability, robustness, fairness, and clinical trust. The paper argues that privacy preservation in wearable health monitoring cannot be achieved through a single algorithmic choice. Rather, it requires an integrated design spanning edge hardware, coordination servers, governance processes, regulatory frameworks, and clinical validation. Cross-domain comparisons with mobile keyboard prediction and multi-institutional medical imaging illustrate both the feasibility and the limitations of federated learning in health contexts. The paper concludes with research directions related to energy-efficient training, multimodal generation, federated evaluation, and accountable governance.