Energy-Efficient Distributed Machine Learning at Wireless Edge with Device-to-Device Communication
Energy-Efficient Distributed Machine Learning at Wireless Edge with Device-to-Device Communication
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DOI：
10.1109/icc45855.2022.9838508
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发表时间：
2022-05
期刊：
影响因子：
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通讯作者：
Rui Hu;Yuanxiong Guo;Yanmin Gong
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文献类型：
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作者：
Rui Hu;Yuanxiong Guo;Yanmin Gong
本文考虑了一种联合边缘学习（FEL）系统，其中基站（BS）协调一组边缘设备来协作地训练共享机器学习模型。这种系统的基本问题之一是在边缘设备的有限和异构资源能力的情况下保持学习性能。我们的目标是通过减轻其能量资源的时间和空间异质性来提高自由电子激光中边缘设备的能量效率。具体地，为了平衡边缘设备之间的异构能量水平，能量消耗大的设备可以经由设备到设备（D2D）通信链路以低传输开销将它们的数据卸载到具有足够能量的附近设备。此外，为了减轻设备的时变能量水平的影响，边缘设备收集的数据可以排队，以便在有足够的能量可用时进行处理。为了计算最佳卸载和排队策略，我们提出了一种基于李亚普诺夫优化的在线控制算法，以确定在每个时隙要卸载，排队和处理的数据量。我们在真实世界数据集上的模拟结果表明，我们的方法实现了比基线更好的整体能源效率。
This paper considers a federated edge learning (FEL) system where a base station (BS) coordinates a set of edge devices to train a shared machine learning model collaboratively. One of the fundamental issues in such systems is maintaining the learning performance with the limited and heterogeneous resource capabilities of edge devices. Our goal is to improve the energy efficiency of edge devices in FEL by mitigating the temporal and spatial heterogeneity of their energy resources. Specifically, to balance the heterogeneous energy levels among edge devices, energy-hungry devices can offload their data to nearby devices that have sufficient energy via device-to-device (D2D) communication links at low transmission overheads. Be-sides, to mitigate the impact of the time-varying energy level of a device, data collected by edge devices can be queued to be processed when sufficient energy is available. To compute the optimal offloading and queuing strategies, we propose an online control algorithm based on Lyapunov optimization to determine the amount of data to be offloaded, queued, and processed at each time slot. Our simulation results on the real-world dataset demonstrate that our approach achieves a better overall energy efficiency than baselines.