多设备协同边缘智能中，用量子启发方法提升公平性与通信效率。
Quantum Machine Learning for Secure Cooperative Multi-Layer Edge AI with Proportional Fairness
- 基于双阈值早退出机制，实现多设备分布式推理
- 在通信、能耗约束下，系统性能提升且用户间公平性改善
- 适合资源受限的多用户协同边缘计算场景
本文提出一种通信高效、事件触发的协作式边缘人工智能推理框架，包含多个用户设备和边缘服务器。在稀有事件检测的双阈值早退出策略基础上，将经典单设备推理扩展至分布式多设备场景，并引入用户间的比例公平性约束。构建联合优化框架，在通信、能量及公平性约束下最大化分类效用。为高效求解，利用效用函数关于置信度阈值的单调性，采用交替优化与Benders分解相结合的方法。实验表明，相比单设备基线，该框架显著提升了系统整体性能与资源分配公平性。
原文摘要 · Abstract (English)
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.
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