Federated Learning–Enabled Privacy-Preserving PPG Foundation Models for Intelligent Healthcare Agent Systems
Keywords:
Federated learning; PPG foundation model; Privacy-preserving AI; Intelligent healthcare agents; System governance; RobustnessAbstract
The convergence of photoplethysmography as a near-ubiquitous non-invasive sensing modality, large-scale health foundation models, and intelligent agent systems promises a transformative leap toward personalized, longitudinal, and scalable healthcare. However, the centralization of sensitive physiological data introduces profound privacy risks and increasingly stringent regulatory constraints. This paper presents a system-level exploration of federated learning–enabled privacy-preserving PPG foundation models designed to serve as sensing and representation backbones within intelligent healthcare agent architectures. We examine the complete pipeline, from on-device self-supervised pre-training of a shared PPG foundation model across heterogeneous wearable populations to its privacy-preserving aggregation and integration into multi-agent reasoning frameworks that deliver context-aware health monitoring, triage, and decision explanation. The analysis foregrounds structural trade-offs among communication efficiency, model utility, differential privacy guarantees, secure aggregation, and fairness across diverse demographic and device strata. Particular attention is given to the governance, sustainability, and deployment implications of such a system, including Byzantine resilience, regulatory compliance with the GDPR and HIPAA, and the challenges of continual adaptation on resource-constrained edge devices. Through conceptual synthesis of recent advances and critical system design perspectives, we argue that a privacy-by-design federated foundation model combined with modular agent orchestration can form the trustworthy infrastructural core of next-generation intelligent healthcare ecosystems. Open research directions concerning adversarial robustness of agent reasoning, cross-silo governance, and post-deployment lifecycle management are identified to inform future interdisciplinary efforts.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
