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

• URL: http://arxiv.org/abs/2508.15821v1
• Date: Sun, 17 Aug 2025 17:09:42 GMT
• Title: Straggler-Resilient Federated Learning over A Hybrid Conventional and Pinching Antenna Network
• Authors: Bibo Wu, Fang Fang, Ming Zeng, Xianbin Wang,
• Abstract summary: Leveraging pinching antennas in wireless network enabled learning (FL) can effectively mitigate the common "straggler" issue in FL.<n>A hybrid conventional and pinching antenna network (HCPAN) is proposed to significantly improve communication efficiency.
• Score: 13.50794408418408
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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