{"ID":2881491,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.15821","arxiv_id":"2508.15821","title":"Straggler-Resilient Federated Learning over A Hybrid Conventional and Pinching Antenna Network","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.","short_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 co...","url_abs":"https://arxiv.org/abs/2508.15821","url_pdf":"https://arxiv.org/pdf/2508.15821v1","authors":"[\"Bibo Wu\",\"Fang Fang\",\"Ming Zeng\",\"Xianbin Wang\"]","published":"2025-08-17T17:09:42Z","proceeding":"cs.IT","tasks":"[\"cs.IT\",\"cs.AI\",\"cs.NI\"]","methods":"[\"Reinforcement Learning\"]","has_code":false}