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arXiv · 2508.15821

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

Bibo Wu·Fang Fang·Ming Zeng·Xianbin Wang
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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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Bibo Wu, Fang Fang, Ming Zeng, Xianbin Wang. 2025-08-17. Straggler-Resilient Federated Learning over A Hybrid Conventional and Pinching Antenna Network. https://arxiv.org/abs/2508.15821

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