An Edge-Cloud Infrastructure for Scalable Personalization of AI Models in Community Health Care

Modern Internet of Things (IoT) systems increasingly rely on machine learning/deep learning models deployed across edge devices and cloud infrastructure. However, many real-world IoT systems require personalized models for each user or device, making scalable retraining, deployment, and real-time inference challenging. To address this, we propose a practical low-cost edge–cloud framework for concurrent personalization of AI models in IoT systems that can support users in a community-dwelling center. The personalization is achieved with a data-efficient machine learning retraining model (MODELXc) coupled with a layered software architecture that separates edge sensing, communication, cloud processing, and model management. First, retraining of AI model jobs runs as Kubernetes pods with pre-configured CPU and memory limits, and users are grouped into batches to prevent resource oversubscription during concurrent training. Second, edge devices communicate with the cloud using NATS.io request-reply messaging with binary-encoded payloads for low-latency communication. Third, a lifecycle pipeline handles versioned model storage, in-memory caching, and background synchronization. We evaluated this framework using two IoT applications: a wearable fall-detection system and a heart-rate-monitoring system. The second application is used to demonstrate the cross application portability of our infrastructure. Using the MODELXc personalization workflow, we executed 40 user-specific training jobs concurrently on a 40-logical-CPU server within one minute. We also demonstrated experimentally that a 40-CPU server can support personalization of 5000 virtual users in a 6–7 h period using a batch-wise processing strategy. Docker- and Kubernetes-based orchestration provided bounded per-job resource usage and resulted in a predictable system load compared with traditional background execution. For real-time inference, NATS.io with binary encoding and controlled throttling achieved a median latency of approximately 110–120 ms, compared with approximately 220 ms using the default without binary encoding.