标题： 鲁棒分段模拟广播设计以加速无线联邦学习 显示英文标题

标题： Robust Segmented Analog Broadcast Design to Accelerate Wireless Federated Learning

摘要： 我们考虑在具有不完美信道状态信息（CSI）的无线网络中联邦学习（FL）的下行广播设计。 旨在减少传输延迟，我们提出了一种分段模拟广播（SegAB）方案，其中由多天线基站托管的参数服务器将全局模型参数向量划分为多个段，并通过公共下行信道同时传输这些段中的多个参数。 我们将SegAB的传输和接收过程进行建模，以表征FL训练的收敛性，捕捉下行波束成形和不完美CSI的影响。 为了最大化FL训练的收敛速度，我们建立了期望模型最优性差距的上界，并表明可以在在线优化中单独针对每个训练轮次进行最小化，而无需了解未来的信道状态。 我们通过最小化最坏情况目标来解决每轮问题，以实现鲁棒的下行波束成形，利用一个等价图表示和一个可行性子问题来确保单调收敛。 在典型无线网络设置下的标准分类任务仿真表明，所提出的SegAB显著优于传统的全模型逐参数广播和其他替代方案。 显示英文摘要

摘要： We consider downlink broadcast design for federated learning (FL) in a wireless network with imperfect channel state information (CSI). Aiming to reduce transmission latency, we propose a segmented analog broadcast (SegAB) scheme, where the parameter server, hosted by a multi-antenna base station, partitions the global model parameter vector into segments and transmits multiple parameters from these segments simultaneously over a common downlink channel. We formulate the SegAB transmission and reception processes to characterize FL training convergence, capturing the effects of downlink beamforming and imperfect CSI. To maximize the FL training convergence rate, we establish an upper bound on the expected model optimality gap and show that it can be minimized separately over the training rounds in online optimization, without requiring knowledge of the future channel states. We solve the per-round problem to achieve robust downlink beamforming, by minimizing the worst-case objective via an epigraph representation and a feasibility subproblem that ensures monotone convergence. Simulation with standard classification tasks under typical wireless network setting shows that the proposed SegAB substantially outperforms conventional full-model per-parameter broadcast and other alternatives.