发表机构
London South Bank University（伦敦南岸大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过ns-3模拟和Bianchi模型估计，预测联邦学习在802.11无线网络中的通信时间成本，发现通信时间随客户端密度增加约两个数量级，而轮次数变化不大。
AI 中文摘要
基于IEEE 802.11的联邦学习在发送模型更新的客户端之间共享无线信道。我们使用ns-3测量不同客户端密度和提供负载下的帧交付率和饱和吞吐量。一个独立的FedAvg训练器将帧交付率作为更新接纳概率的一阶代理，并使用一个方程来估计通信时间。该方法不模拟完整模型更新的交付，也不测量端到端训练时间。在720次评估运行中，涉及两个数据集、两种数据分区、六种客户端密度、六种提供负载和五种随机种子，所有运行都在轮次预算内达到了预定的目标精度。达到目标的轮次数随提供负载变化不大，而达到目标的通信时间在客户端密度范围内增加了大约两个数量级。一个基于Bianchi的估计器在保留配置上产生了从2.3%到10.2%的平均绝对百分比误差。该误差是针对由相同轮次持续时间方程构建的通信时间测量的，而非针对独立测量的完成时间。我们还比较了均匀参与和持续异质参与。该研究在五种随机种子上未检测到统计上可区分的排除类精度差距，但置信区间较宽。结果仅适用于评估的配置，不提供一般的收敛性或公平性保证。
英文摘要
Federated learning over IEEE~802.11 shares the wireless channel among clients that send model updates. We use ns-3 to measure the frame-delivery ratio and saturation throughput for different client densities and offered loads. A separate FedAvg trainer uses the frame-delivery ratio as a first-order proxy for the update-admission probability and uses an equation to estimate communication time. The method does not simulate the delivery of a complete model update or measure end-to-end training time. Across 720 evaluated runs with two datasets, two data partitions, six client densities, six offered loads, and five seeds, all runs reached their predefined target accuracy within the round budget. Rounds-to-target changed little with offered load, while communication time-to-target increased by about two orders of magnitude across the client-density range. A Bianchi-anchored estimator produced a mean absolute percentage error from $2.3\%$ to $10.2\%$ on held-out configurations. This error is measured against communication time constructed from the same round-duration equation, not against independently measured completion time. We also compare uniform participation with persistent heterogeneous participation. The study does not detect a statistically distinguishable excluded-class accuracy gap over five seeds, but the confidence intervals are wide. The results apply only to the evaluated configurations and do not provide a general convergence or fairness guarantee.
