Motivated by the increasing computational capacity of wireless user equipments (UEs), e.g., smart phones, tablets, or vehicles, as well as the increasing concerns about sharing private data, a new machine learning model has emerged, namely federated learning (FL), that allows a decoupling of data acquisition and computation at the central unit. Unlike centralized learning taking place in a data center, FL usually operates in a wireless edge network where the communication medium is resource-constrained and unreliable. Due to limited bandwidth, only a portion of UEs can be scheduled for updates at each iteration. Due to the shared nature of the wireless medium, transmissions are subjected to interference and are not guaranteed. The performance of FL system in such a setting is not well understood. In this paper, an analytical model is developed to characterize the performance of FL in wireless networks. Particularly, tractable expressions are derived for the convergence rate of FL in a wireless setting, accounting for effects from both scheduling schemes and inter-cell interference. Using the developed analysis, the effectiveness of three different scheduling policies, i.e., random scheduling (RS), round robin (RR), and proportional fair (PF), are compared in terms of FL convergence rate. It is shown that running FL with PF outperforms RS and RR if the network is operating under a high signal-to-interference-plus-noise ratio (SINR) threshold, while RR is more preferable when the SINR threshold is low. Moreover, the FL convergence rate decreases rapidly as the SINR threshold increases, thus confirming the importance of compression and quantization of the update parameters. The analysis also reveals a trade-off between the number of scheduled UEs and subchannel bandwidth under a fixed amount of available spectrum.

中文翻译：

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无线网络中联合学习的调度策略

受无线用户设备 (UE)（例如智能手机、平板电脑或车辆）日益增长的计算能力以及对共享私人数据的日益关注的推动，出现了一种新的机器学习模型，即联邦学习 (FL)，这允许在中央单元上分离数据采集和计算。与在数据中心进行的集中学习不同，FL 通常在无线边缘网络中运行，其中通信介质资源受限且不可靠。由于带宽有限，每次迭代只能调度一部分 UE 进行更新。由于无线介质的共享特性，传输会受到干扰并且无法保证。FL 系统在这种设置下的性能还不是很清楚。在本文中，开发了一个分析模型来表征无线网络中 FL 的性能。特别是，在无线环境中，FL 收敛率的易处理表达式被推导出来，考虑了调度方案和小区间干扰的影响。使用开发的分析，三种不同调度策略的有效性，即随机调度 (RS)、循环 (RR) 和比例公平 (PF)，在 FL 收敛速度方面进行了比较。结果表明，如果网络在高信噪比 (SINR) 阈值下运行，使用 PF 运行 FL 的性能优于 RS 和 RR，而当 SINR 阈值较低时，RR 更可取。此外，随着 SINR 阈值的增加，FL 收敛速度迅速下降，从而证实了更新参数的压缩和量化的重要性。该分析还揭示了在固定数量的可用频谱下调度的 UE 数量和子信道带宽之间的权衡。