用可调天线网络优化联邦学习通信，解决慢节点问题。
Straggler-Resilient Federated Learning over A Hybrid Conventional and Pinching Antenna Network
- 用模糊逻辑分类客户端，平衡数据贡献与通信条件。
- 通过深度强化学习优化天线部署与资源分配，减少总训练时间。
- 适合研究无线联邦学习、智能天线系统的研究者参考。
在无线联邦学习（FL）中，利用可调天线能动态建立强视距（LoS）链路，有效缓解常见的“慢节点”问题。本文提出一种混合传统与可调天线网络（HCPAN），显著提升非正交多址（NOMA）赋能的联邦学习系统中的通信效率。首先，设计基于模糊逻辑的客户端分类方案，以平衡各客户端的数据贡献与通信状况。在此基础上，构建总时间最小化问题，联合优化可调天线位置与资源分配。由于变量耦合复杂且目标函数非凸，提出一种基于深度强化学习（DRL）的算法求解该问题。仿真结果验证了所提方案在优化可调天线部署后，能有效提升联邦学习性能。
原文摘要 · Abstract (English)
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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