发表机构
Broadband Access Network Laboratory, Shanghai Jiao Tong University; School of Information Science and Engineering, Southeast University; Data61, CSIRO; Department of Electronic and Electrical Engineering, University College London; School of Internet of Things Engineering, Jiangnan University（宽带接入网络实验室，上海交通大学; 信息科学与工程学院，东南大学; 数据61，联邦科学与工业研究组织; 电子与电气工程系，伦敦大学学院; 物联网工程学院，江南大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对无线网络联邦学习中训练延迟和收敛性问题，考虑RIS辅助阻塞链路场景，通过刻画符号错误影响得出收敛上限，将联合收敛-延迟优化问题转化为MINLP问题，用低复杂度框架求解，实验表明该方案收敛快、准确率高。
AI 中文摘要
无线网络上的联邦学习因不可靠的无线传输而存在显著训练延迟且收敛性下降，尤其在阻塞传播环境下。尽管可重构智能表面（RIS）可提高通信可靠性，但现有无线联邦学习研究很少在依赖调制的传输错误下刻画学习收敛与通信延迟之间的权衡。本文考虑在RIS辅助的阻塞链路传播场景下运行的无线联邦学习系统，专注于收敛-延迟感知通信设计的自适应调制和子信道分配。通过刻画符号错误对上传的局部梯度的影响，得出一个与收敛相关的上限，揭示符号错误率对联邦学习损失衰减的影响。基于此结果，制定联合收敛-延迟优化问题，将其转化为混合整数非线性规划（MINLP）问题，并使用低复杂度混合交替优化框架求解。在MNIST、CIFAR-10和语音命令上的大量实验表明，所提方案始终比现有自适应通信方案实现更快收敛和更高测试准确率，尤其在复杂任务和具有挑战性的无线场景中。
英文摘要
Federated learning (FL) over wireless networks suffers from significant training latency and degraded convergence due to unreliable wireless transmission, especially under blocked propagation environments. Although reconfigurable intelligent surfaces (RISs) can improve communication reliability, existing wireless FL studies rarely characterize the trade-off between learning convergence and communication delay under modulation-dependent transmission errors. In this paper, we consider a wireless FL system operating under RIS-assisted blocked-link propagation scenarios, and focus on adaptive modulation and sub-channel allocation for convergence-latency aware communication design. By characterizing the effect of symbol errors on uploaded local gradients, we derive a convergence-related upper bound that reveals the impact of symbol error rate (SER) on FL loss decay. Based on this result, we formulate a joint convergence-latency optimization problem, which is cast as a mixed-integer nonlinear programming (MINLP) problem, and solve it using a low-complexity hybrid alternating optimization framework. Extensive experiments on MNIST, CIFAR-10, and Speech Commands show that the proposed scheme consistently achieves faster convergence and higher test accuracy than existing adaptive communication schemes, especially in complex tasks and challenging wireless scenarios.
