Resource Management for Edge-Assisted Learning with Deterministic Reliability Constraints

We consider a novel resource allocation framework designed to achieve optimal resource management for edgeassisted inference tasks. This is obtained by a new optimization approach, addressed as conformal Lyapunov optimization, which integrates online conformal risk control (O-CRC) with conventional Lyapunov optimization (LO). Unlike traditional LO, this approach ensures compliance with deterministic long-term reliability constraints. Simulation results, based on an edgeassisted segmentation task, demonstrate the effectiveness of the proposed method in balancing energy consumption and inference performance, while maintaining strict control over deterministic long-term constraints, related to the false negative segmentation rate.