Straggler-Resilient Federated Learning over A Hybrid Conventional and Pinching Antenna Network
This is an incremental improvement for wireless network-enabled federated learning systems, addressing communication bottlenecks.
The paper tackles the straggler problem in federated learning by proposing a hybrid conventional and pinching antenna network to improve communication efficiency, with simulation results validating enhanced performance through optimized deployment.
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.