aiqbench analysis
This paper tackles a wireless engineering problem: how to choose devices, beamforming, and pinching-antenna positions so that over-the-air federated learning can aggregate updates from as many devices as possible while keeping distortion under a threshold. The core contribution is an alternating optimization method for a nonconvex problem, so the results are mainly relevant to communications researchers. ML readers should note that the gains are measured in device selection and aggregation quality for federated training, not in model accuracy or learning algorithms.
AirPASS: Over-the-Air Federated Learning via Pinching Antenna Systems
arXiv:2607.06768v2 Announce Type: replace-cross 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.
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.
Information Theory (cs.IT); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
arXiv:2607.06768 [cs.IT]
(or arXiv:2607.06768v2 [cs.IT] for this version)
https://doi.org/10.48550/arXiv.2607.06768 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 218 words
