AirPASS: Over-the-Air Federated Learning via Pinching Antenna Systems

@misc{azimiabarghouyi2026airpass,
  title = {{AirPASS: Over-the-Air Federated Learning via Pinching Antenna Systems}},
  author = {Seyed Mohammad Azimi-Abarghouyi and Christopher G. Brinton},
  howpublished = {arXiv},
  year = {2026},
  month = oct,
  doi = {10.48550/arxiv.2607.06768},
  eprint = {2607.06768},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2607.06768},
}

TY  - UNPB
TI  - AirPASS: Over-the-Air Federated Learning via Pinching Antenna Systems
AU  - Seyed Mohammad Azimi-Abarghouyi
AU  - Christopher G. Brinton
PY  - 2026
DA  - 2026/10/21
PB  - arXiv
DO  - 10.48550/arxiv.2607.06768
UR  - https://arxiv.org/abs/2607.06768
AB  - 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.
ER  -