Motivated by the ever-increasing demands for massive data processing and intelligent data analysis at the network edge, federated learning (FL), a distributed architecture for machine learning, has been introduced to enhance edge intelligence without compromising data privacy. Nonetheless, due to the large number of edge devices (referred to as clients in FL) with only limited wireless resources, client scheduling, which chooses only a subset of devices to participate in each round of FL, becomes a more feasible option. Unfortunately, the training latency can be intolerable in the iterative process of FL. To tackle the challenge, this article introduces update-importance-based client scheduling schemes to reduce the required number of rounds. Then latency-based client scheduling schemes are proposed to shorten the time interval for each round. We consider the scenario where no prior information regarding the channel state and the resource usage of the devices is available, and propose a scheme based on the multi-armed bandit theory to strike a balance between exploration and exploitation. Finally, we propose a latency-based technique that exploits update importance to reduce the training time. Computer simulation results are presented to evaluate the convergence rate with respect to the rounds and wall-clock time consumption.

中文翻译：

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基于联邦学习的低延迟无线通信客户端调度


在网络边缘对海量数据处理和智能数据分析日益增长的需求的推动下，联邦学习（FL）这种分布式机器学习架构被引入，以在不损害数据隐私的情况下增强边缘智能。尽管如此，由于大量边缘设备（在 FL 中称为客户端）的无线资源有限，仅选择设备子集参与每轮 FL 的客户端调度成为更可行的选择。不幸的是，FL 的迭代过程中的训练延迟可能是无法容忍的。为了应对这一挑战，本文引入了基于更新重要性的客户端调度方案，以减少所需的轮数。然后提出基于延迟的客户端调度方案来缩短每轮的时间间隔。我们考虑了没有关于信道状态和设备资源使用的先验信息的情况，并提出了一种基于多臂老虎机理论的方案，以在探索和利用之间取得平衡。最后，我们提出了一种基于延迟的技术，利用更新重要性来减少训练时间。提供计算机模拟结果来评估相对于轮数和挂钟时间消耗的收敛速度。