Straggler-Resilient Federated Learning over A Hybrid Conventional and Pinching Antenna Network

Bibo Wu, Fang Fang, Ming Zeng, Xianbin Wang
8/17/2025
cs.ITcs.AIcs.NI

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Abstract

Leveraging pinching antennas in wireless network enabled federated learning (FL) can effectively mitigate the common "straggler" issue in FL by dynamically establishing strong line-of-sight (LoS) links on demand. This letter proposes a hybrid conventional and pinching antenna network (HCPAN) to significantly improve communication efficiency in the non-orthogonal multiple access (NOMA)-enabled FL system. Within this framework, a fuzzy logic-based client classification scheme is first proposed to effectively balance clients' data contributions and communication conditions. Given this classification, we formulate a total time minimization problem to jointly optimize pinching antenna placement and resource allocation. Due to the complexity of variable coupling and non-convexity, a deep reinforcement learning (DRL)-based algorithm is developed to effectively address this problem. Simulation results validate the superiority of the proposed scheme in enhancing FL performance via the optimized deployment of pinching antenna.

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Cite this paper

@article{wu2025stragglerresilient,
  title  = {Straggler-Resilient Federated Learning over A Hybrid Conventional and Pinching Antenna Network},
  author = {Bibo Wu and Fang Fang and Ming Zeng and Xianbin Wang},
  year   = {2025},
  eprint = {2508.15821},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2508.15821v1}
}

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