Federated learning (FL) over wireless networks is fundamentally constrained by unreliable communication links, particularly when uplink channels suffer from blockage, fading, or weak line-of-sight (LoS) conditions. Pinching-antenna systems (PASSs) offer a new physical-layer capability to dynamically reposition radiating points along a dielectric waveguide, enabling controllable LoS connectivity and significantly improved channel quality. This paper develops FedPASS, a novel framework for low-latency wireless FL assisted by PASS. We formulate a multi-objective optimization problem that jointly minimizes the end-to-end round latency and an upper bound on the FL optimality gap. The resulting formulation is a mixed-integer nonlinear program subject to practical constraints on scheduling, transmit power, local CPU frequency, and PA placement. To address the resulting computational challenges, we develop a two-tier iterative algorithm: an outer loop that updates scheduling, communication time allocation, and power control via block coordinate descent, and an inner loop that optimizes PA locations using a Gauss-Seidel-based coordinate update with grid search under spacing constraints. Numerical results on MNIST and CIFAR-10 demonstrate that FedPASS achieves accuracy comparable to idealized FL baselines while drastically reducing the total training latency compared to conventional wireless FL.

翻译：无线网络中的联邦学习（FL）从根本上受到不可靠通信链路的制约，尤其当上行链路信道遭受阻塞、衰落或弱视距（LoS）条件时。夹持天线系统（PASS）提供了一种新的物理层能力，可沿介质波导动态重新定位辐射点，从而实现可控的视距连接并显著提升信道质量。本文提出了FedPASS，一种由PASS辅助的低延迟无线联邦学习新框架。我们构建了一个多目标优化问题，联合最小化端到端轮次延迟和FL最优性间隙的上界。所得公式是一个混合整数非线性规划，受限于调度、发射功率、本地CPU频率和PA放置的实际约束。为解决由此产生的计算挑战，我们开发了一种双层迭代算法：外层循环通过块坐标下降更新调度、通信时间分配和功率控制；内层循环在间距约束下，采用基于高斯-塞德尔的坐标更新与网格搜索优化PA位置。在MNIST和CIFAR-10上的数值结果表明，FedPASS实现了与理想化FL基线相当的精度，同时相比传统无线FL大幅降低了总训练延迟。