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

arXiv: https://arxiv.org/abs/2508.15821 · HTML: https://mlsanity.com/paper/2508.15821/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 22 references that are in the map.
- First posted on arXiv 17 Aug 2025.

## Builds on (most cited first, 11 of 11)

- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Wireless Communications for Collaborative Federated Learning](https://mlsanity.com/paper/2006.02499/) (2020)
- [Reconfigurable Intelligent Surface Enabled Federated Learning: A Unified Communication-Learning Design Approach](https://mlsanity.com/paper/2011.10282/) (2020)
- [Integrating Over-the-Air Federated Learning and Non-Orthogonal Multiple Access: What Role can RIS Play?](https://mlsanity.com/paper/2103.00435/) (2021)
- [Flexible-Antenna Systems: A Pinching-Antenna Perspective](https://mlsanity.com/paper/2412.02376/) (2024)
- [Multi-Carrier NOMA-Empowered Wireless Federated Learning with Optimal Power and Bandwidth Allocation](https://mlsanity.com/paper/2302.06730/) (2023)
- [Antenna Activation for NOMA Assisted Pinching-Antenna Systems](https://mlsanity.com/paper/2412.13969/) (2024)
- [Rate Maximization for Downlink Pinching-Antenna Systems](https://mlsanity.com/paper/2502.12629/) (2025)
- [A Low-Complexity Placement Design of Pinching-Antenna Systems](https://mlsanity.com/paper/2502.14250/) (2025)
- [Federated Learning Meets Fluid Antenna: Towards Robust and Scalable Edge Intelligence](https://mlsanity.com/paper/2503.03054/) (2025)
- [Movable Antenna Enhanced Federated Fine-Tuning of Large Language Models via Hybrid Client Selection Optimization](https://mlsanity.com/paper/2506.00011/) (2025)

## Built on by (most cited first, 1 of 1)

- [Pinching Antennas Meet AI in Next-Generation Wireless Networks](https://mlsanity.com/paper/2511.07442/) (2025)

## Abstract (arXiv)

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.