基于极化码的联邦学习：收敛性分析与资源分配

中文摘要 AI 辅助

联邦学习（FL）可实现分布式设备间的协作模型训练，无需共享原始数据；但在实际应用中，它面临显著的通信瓶颈和信道损伤问题。传统网络层处理要么将信道理想化视为无差错，要么对传输的模型更新应用均等错误保护（EEP），未能考虑单个本地模型内量化比特的固有重要性差异。为解决这一局限，我们提出一种基于极化码的跨层联邦学习方案，该方案利用有限块长下极化码的不等错误保护（UEP）特性。具体而言，所提设计选择性地保护更重要的量化比特，从而减轻信道噪声的有害影响。我们还对该方案进行了严格的收敛性分析，推导了收敛间隙的上界，随后在所有训练迭代中联合优化量化比特数量和极化码块长。实验结果表明，我们的基于极化码的方案，无论是固定块长还是可变块长配置，均比无编码方案和基于LDPC的EEP基准实现了显著的性能提升，且随着信道质量恶化，该优势愈发明显。这些发现证实了我们的跨层设计在实际信道条件下增强联邦学习鲁棒性和效率的有效性。

英文摘要

Federated learning (FL) enables collaborative model training across distributed devices without sharing raw data; however, it faces significant communication bottlenecks and channel impairments in practice. Conventional network layer treatments either idealize the channel as error free or apply equal error protection (EEP) to transmitted model updates, failing to account for the inherently unequal importance of quantization bits within a single local model. To address this limitation, we propose a cross layer polar code based FL scheme that leverages the unequal error protection (UEP) property of polar codes under finite block lengths. Specifically, the proposed design selectively protects more significant quantization bits, thereby mitigating the detrimental effects of channel noise. We further provide a rigorous convergence analysis of the proposed scheme, deriving an upper bound on the convergence gap, which we then jointly optimize over the number of quantization bits and the polar code block length across all training iterations. Experimental results demonstrate that both constant and variable block length configurations of our polar code based scheme consistently achieve substantial performance gains over uncoded and LDPC-based EEP benchmarks, with the advantage becoming increasingly pronounced as the channel quality deteriorating. These findings confirm the efficacy of our cross-layer design in enhancing FL robustness and efficiency under realistic channel conditions.

发表机构

• School of Information Science and Engineering, Southeast University（东南大学信息科学与工程学院）
• National Mobile Communications Research Laboratory, Southeast University（东南大学国家移动通信重点实验室）

机构由 AI 辅助整理，请以论文原文为准。