Energy-Sustainable Federated AI Services for Ubiquitous Remote Patient Monitoring in Battery-Constrained IoMT

Abstract

AI-driven Internet of Medical Things (IoMT) services increasingly rely on federated learning (FL) for privacy-preserving remote patient monitoring; however, existing frameworks often neglect the longitudinal battery sustainability required for persistent clinical care. This paper presents BattFL, an energy-sustainable federated AI framework that reframes distributed healthcare intelligence as a battery-budgeted service. By integrating explicit per-client battery evolution modeling with computation-communication-security energy decomposition, BattFL supports role-heterogeneous participation (attack-only, health-only, and hybrid) via masked multi-task learning. Furthermore, we incorporate risk-driven adaptive sensing to regulate workload intensity based on predicted clinical risk. Experimental results reveal a pronounced sustainability asymmetry: high-workload attack clients experience up to 41.2% battery depletion within five FL rounds, while health-only clients remain near initial capacity. Across extended horizons $(R=5-60)$, we identify diminishing energy-accuracy returns and participation decay, motivating the need for energy-aware stopping and orchestration mechanisms. By explicitly coupling battery dynamics, security overhead, and federated optimization, BattFL provides a systems-level foundation for secure, resilient, and long-lived AI-driven healthcare services in next-generation ubiquitous IoMT networks.