arXiv cs.AI· Seyed Mohammad Azimi-Abarghouyi, Christopher G. Brinton·· 2 天前AI 评分10
AirPASS：基于 Pinching 天线系统的空中联邦学习
AirPASS: Over-the-Air Federated Learning via Pinching Antenna Systems
AI 导读
该研究提出 AirPASS，一个面向配备多波导 Pinching 天线系统（PASS）接入点的空中联邦学习（AirFL）交替优化框架，在聚合失真低于预设阈值的前提下最大化被选中设备数量。
正文 · 原文
Abstract: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 a fixed PASS configuration, and a homotopy-assisted geometry optimization method for updating the pinching-antenna positions under fixed selected devices and beamformer.
| Subjects: | Information Theory (cs.IT); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2607.06768 [cs.IT] |
| (or arXiv:2607.06768v2 [cs.IT] for this version) | |
| https://doi.org/10.48550/arXiv.2607.06768 arXiv-issued DOI via DataCite |
Submission history
From: Seyed Mohammad Azimi-Abarghouyi [view email]
[v1]
Tue, 7 Jul 2026 19:55:46 UTC (975 KB)
[v2]
Wed, 14 Oct 2026 23:02:59 UTC (1,132 KB)
来源：arXiv cs.AI · arxiv.org