{"ID":6138875,"CreatedAt":"2026-07-09T01:07:32.349475501Z","UpdatedAt":"2026-07-10T19:50:46.179895015Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.06768","arxiv_id":"2607.06768","title":"AirPASS: Over-the-Air Federated Learning via Pinching Antenna Systems","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 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.","short_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 aggregat...","url_abs":"https://arxiv.org/abs/2607.06768","url_pdf":"https://arxiv.org/pdf/2607.06768v1","authors":"[\"Seyed Mohammad Azimi-Abarghouyi\",\"Christopher G. Brinton\"]","published":"2026-07-07T19:55:46Z","proceeding":"cs.IT","tasks":"[\"cs.IT\",\"cs.AI\",\"cs.LG\"]","methods":"[]","has_code":false}