通过可调天线系统实现高效无线联邦学习，提升设备协同训练性能。
AirPASS: Over-the-Air Federated Learning via Pinching Antenna Systems
- 采用交替优化框架，联合设计设备选择与波束成形。
- 在固定天线配置下，显著降低聚合失真，逼近理想联邦平均效果。
- 适合大规模无线设备协同训练场景，兼顾性能与计算开销。
本文研究在接入点配备多波导捏合天线系统（PASS）的无线系统中的过空气联邦学习（AirFL）。采用广泛研究的学习导向AirFL范式，旨在最大化选中设备数的同时，将聚合失真控制在预设阈值内。由于设备选择、接收波束成形与捏合天线位置之间的复杂耦合，该联合优化问题高度非凸。为此，本文提出AirPASS，一种包含两部分的交替优化框架：在固定PASS配置下，采用同伦-黎曼边界强化方法进行设备选择与接收波束成形；在固定选中设备与波束成形器下，采用同伦辅助几何优化方法更新捏合天线位置。实验表明，AirPASS持续优于传统共置MIMO基线，接近理想FedAvg表现，并在性能-复杂度权衡上优于SDR-DC与匹配追踪调度方案。
原文摘要 · Abstract (English)
This paper investigates over-the-air federated learning (AirFL) in wireless systems where the access point is equipped with a multi-waveguide pinching antenna system (PASS). We adopt the widely studied learning-oriented AirFL formulation, which seeks to maximize the number of selected devices while keeping the aggregation distortion below a prescribed threshold. The resulting joint optimization of device selection, receive beamforming, and pinching-antenna placement is highly nonconvex due to the intricate coupling among these system variables. To address this challenge, we develop AirPASS, an alternating optimization framework with two main components: a homotopy-Riemannian margin-consolidation method for device selection and receive beamforming under fixed PASS configuration, and a homotopy-assisted geometry optimization method for updating the pinching-antenna positions under fixed selected devices and beamformer. Experiments show that AirPASS consistently outperforms conventional co-located MIMO baselines, remains close to ideal FedAvg, and achieves an attractive performance-complexity tradeoff relative to SDR-DC and matching-pursuit scheduling alternatives.
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