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

2025/08/17 by Wu, Bibo, Fang, Fang, Zeng, Ming +1 · 3 citations
#Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Information Theory (cs.IT) #Networking and Internet Architecture (cs.NI)

paper · doi:10.48550/arxiv.2508.15821

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.

Citations

• Pinching Antennas Meet AI in Next-Generation Wireless Networks
• Movable Antenna Enhanced Federated Fine-Tuning of Large Language Models via Hybrid Client Selection Optimization
• Federated Learning Meets Fluid Antenna: Towards Robust and Scalable Edge Intelligence
• A Low-Complexity Placement Design of Pinching-Antenna Systems
• Rate Maximization for Downlink Pinching-Antenna Systems
• Antenna Activation for NOMA Assisted Pinching-Antenna Systems
• Flexible-Antenna Systems: A Pinching-Antenna Perspective
• Multi-Carrier NOMA-Empowered Wireless Federated Learning with Optimal Power and Bandwidth Allocation
• Reconfigurable Intelligent Surface Enabled Federated Learning: A Unified Communication-Learning Design Approach
• Wireless Communications for Collaborative Federated Learning
• Continuous control with deep reinforcement learning

Cited by

• Age-of-Information Aware Federated Learning with Finite Speed Pinching Antenna
• Pinching Antennas Meet AI in Next-Generation Wireless Networks
• Pinching-antenna-enabled Federated Learning: Tail Latency, Participation, and Convergence Analysis

Related

• Pinching-antenna-enabled Federated Learning: Tail Latency, Participation, and Convergence Analysis
• Age-of-Information Aware Federated Learning with Finite Speed Pinching Antenna
• Multi-Carrier NOMA-Empowered Wireless Federated Learning with Optimal Power and Bandwidth Allocation
• Federated Learning and Wireless Communications
• Joint Age-based Client Selection and Resource Allocation for Communication-Efficient Federated Learning over NOMA Networks