AI 中文总结
研究无线网络中基于有限速度收缩天线的信息年龄感知联邦学习，通过联合优化设备集与天线位置制定AoI最小化问题，提出联盟博弈设备选择算法及分支定界天线放置算法，相比基线方案有更好效果。
AI 中文摘要
本文研究了基于无线网络中有限速度收缩天线的信息年龄（AoI）感知联邦学习。与现有假设天线移动速度无限高的研究不同，考虑了一种基于轮次的实际训练过程，其中收缩天线在本地训练阶段重新定位，其可行移动范围取决于所选设备。这在设备选择、天线放置、本地训练时间、模型上传时间和AoI演化之间产生了新的耦合。为了表征天线移动速度的影响，分析了相对于固定天线的速率增益以及与无限速度基准的差距。随后，通过联合优化所选设备集和收缩天线位置，在轮次延迟期限下制定了总体AoI最小化问题。提出了一种基于联盟博弈的设备选择算法，其中将有限速度天线放置纳入联盟效用评估。对于天线放置，通过利用移动性约束和设备位置跨度得出最优搜索区域，并在此基础上开发了一种分支定界（BnB）算法以获得全局最优解。仿真结果表明，与基线方案相比，所提方案能够加速学习收敛、降低总AoI并提高设备参与度，证明了收缩天线通过灵活的空间重新配置增强联邦学习的潜力。
英文摘要
This paper investigates age-of-information (AoI) aware federated learning over wireless networks with finite speed pinching antennas. In contrast to existing studies that assume an infinitely high antenna moving speed, a practical round based training procedure is considered, where the pinching antenna is repositioned during the local training phase and its feasible movement range depends on the selected devices. This creates a new coupling among device selection, antenna placement, local training time, model uploading time, and AoI evolution. To characterize the impact of antenna moving speed, the rate gain over the fixed antenna and the gap to the infinite speed benchmark are analyzed. Subsequently, an overall AoI minimization problem is formulated under a round latency deadline by jointly optimizing the selected device set and the pinching antenna position. A coalitional game based device selection algorithm is proposed, where finite speed antenna placement is incorporated into the coalition utility evaluation. For antenna placement, the optimal search region is derived by exploiting the mobility constraint and device location span, based on which a branch-and-bound (BnB) algorithm is developed to obtain the global optimum. Simulation results show that the proposed scheme can accelerate learning convergence, reduce the sum AoI, and improve device participation compared with baseline schemes, demonstrating the potential of pinching antennas for enhancing federated learning through flexible spatial reconfiguration.
