Quantum Machine Learning for Secure Cooperative Multi-Layer Edge AI with Proportional Fairness

Abstract--This paper proposes a communication-efficient, event-tri ggered inference framework for cooperative edge AI systems comprising multiple user devices and edge servers. Buildin g upon dual-threshold early-exit strategies for rare-even t detection, the proposed approach extends classical single-device infere nce to a distributed, multi-device setting while incorpora ting proportional fairness constraints across users. A joint optimization fr amework is formulated to maximize classification utility un der communication, energy, and fairness constraints. T o solve the resulting pr oblem efficiently, we exploit the monotonicity of the utilit y function with respect to the confidence thresholds and apply alternating optimiza tion with Benders decomposition. Experimental results sho w that the proposed framework significantly enhances system-wide per formance and fairness in resource allocation compared to si ngle-device baselines.