FedQML-Edge：面向通信受限路侧联邦学习的紧凑量子特征概要
FedQML-Edge: Compact Quantum Feature Sketches for Communication-Constrained Roadside Federated Learning
AI总结：
FedQML-Edge为通信受限路侧联邦学习提出联邦量子特征概要流水线，可降低通信量、保护隐私，在交通稳定性门控任务上性能优于经典概要且接近大型MLP。
AI中文摘要：
支持联网自动驾驶车辆走廊的路侧单元（RSU）需要紧凑模型来决定何时应奖励、推迟或禁用协同操作。原始传感器流和神经网络权重检查点不适用于带宽受限、隐私敏感的路侧学习。本文提出FedQML-Edge，一种用于交通稳定性门控的联邦量子特征概要流水线。每个RSU构建交通状态摘要并将电路输入发送至量子计算机；泡利期望形成由逻辑分类器处理的非线性概要。仅分类器更新会共享至聚合器，其头部支持奖励门控。原始观测、车辆记录、事件轨迹和量子概要均保持隐私。我们使用NGSIM轨迹、带传感噪声的SUMO预测门控及IBM量子硬件评估该方法。在NGSIM上，泡利概要相较最强匹配经典概要将测试对数损失降低14.4%；在SUMO上，其稳定窗口召回率接近更大的多层感知器（MLP），同时每轮通信量减少7至28倍。
英文摘要：
Roadside units (RSUs) supporting connected and autonomous vehicle corridors need compact models to decide when cooperative maneuvers should be rewarded, deferred, or disabled. Raw sensor streams and neural network weight checkpoints are poorly suited to bandwidth-limited, privacy-sensitive roadside learning. This paper presents $\texttt{FedQML-Edge}$, a federated quantum feature-sketching pipeline for traffic-stability gating. Each RSU constructs a traffic-state summary and sends circuit inputs to a quantum computer; Pauli expectations form a nonlinear sketch processed by a logistic classifier. Only classifier updates are shared with an aggregator, whose head supports reward gating. Raw observations, vehicle records, event traces, and quantum sketches remain private. We evaluate the method using NGSIM trajectories, SUMO predictive gating with sensing noise, and IBM Quantum hardware. On NGSIM, the Pauli sketch reduces test log loss by $14.4\%$ relative to the strongest matched classical sketch. On SUMO, it approaches larger MLPs in stable-window recall while using $7-28$ times less communication per round.
