Quantum Machine Learning for Secure Cooperative Multi-Layer Edge AI with Proportional Fairness
This work addresses resource allocation and fairness challenges in multi-device edge AI systems, representing an incremental advancement over single-device methods.
The paper tackles the problem of communication-efficient, fair inference in cooperative edge AI systems by proposing a joint optimization framework that maximizes classification utility under constraints. Experimental results show significant improvements in system-wide performance and fairness compared to single-device baselines.
This paper proposes a communication-efficient, event-triggered inference framework for cooperative edge AI systems comprising multiple user devices and edge servers. Building upon dual-threshold early-exit strategies for rare-event detection, the proposed approach extends classical single-device inference to a distributed, multi-device setting while incorporating proportional fairness constraints across users. A joint optimization framework is formulated to maximize classification utility under communication, energy, and fairness constraints. To solve the resulting problem efficiently, we exploit the monotonicity of the utility function with respect to the confidence thresholds and apply alternating optimization with Benders decomposition. Experimental results show that the proposed framework significantly enhances system-wide performance and fairness in resource allocation compared to single-device baselines.