Edge-to-Cloud Continual Learning for Privacy-Preserving Chronic Disease Management

Abstract

Chronic disease management requires continuous monitoring and adaptive treatment strategies, yet traditional healthcare systems suffer from fragmented data collection and reactive interventions. This study presents an edge-to-cloud continual learning architecture that integrates wearable biosensor networks, longitudinal patient data, and privacy-preserving machine learning to enable personalized treatment recommendations. The system employs a three-tier computational model: edge devices perform low-latency real-time signal processing (135 ms), cloud servers provide secure storage and federated model aggregation, and continual learning algorithms adapt treatment plans as patient conditions evolve. The architecture implements iCaRL-based incremental learning with K=500 exemplar replay, combined with differential privacy (ε=2.1) and Paillier homomorphic encryption to protect patient confidentiality during model updates. A prospective clinical validation study enrolled N=132 patients (diabetes n=77, cardiac n=30, respiratory n=25) across three urban clinics over 12 weeks. The edge-to-cloud system achieved 92.6% treatment recommendation accuracy (95% CI: 90.1-95.1%), representing a 6% improvement over cloud-only baseline (86.8%) and 17.6% improvement over static models (75.0%). The hybrid architecture reduced end-to-end latency by 65.3% compared to cloud-only processing (255 ms vs. 735 ms), meeting the <2-second requirement for acute clinical alerts. Privacy evaluation demonstrated membership inference attack AUC of 0.50 (indicating formal privacy safety, threshold ≤0.55) while maintaining clinical accuracy. Backward transfer analysis showed 98.1% retention of prior knowledge after 100 learning rounds, with only 0.2% degradation, demonstrating effective mitigation of catastrophic forgetting. These results establish the feasibility of privacy-preserving, adaptive chronic disease management systems that combine edge intelligence with cloud-based population learning while maintaining patient confidentiality and clinical effectiveness.