Energy Minimization for Federated Asynchronous Learning on Battery-Powered Mobile Devices via Application Co-running
Energy Minimization for Federated Asynchronous Learning on Battery-Powered Mobile Devices via Application Co-running
复制标题
DOI：
10.1109/icdcs54860.2022.00095
复制
发表时间：
2022-04
期刊：
影响因子：
--
通讯作者：
Cong Wang;Bin Hu;Hongyi Wu
中科院分区：
文献类型：
--
作者：
Cong Wang;Bin Hu;Hongyi Wu
能源是大规模联邦系统中必不可少但经常被遗忘的方面。由于大多数研究集中在解决机器学习算法的计算和统计异质性，对移动的系统的影响仍然不清楚。在本文中，我们设计和实现了一个在线优化框架，通过连接异步执行的联邦训练与应用程序的共同运行，以最大限度地减少电池供电的移动的设备上的能源消耗。从一系列实验中，我们发现在后台与前台应用程序共同运行训练过程会给系统带来很大的能量折扣，而性能下降可以忽略不计。基于这些结果，我们首先研究了一个离线问题，假设所有未来发生的应用程序是可用的，并提出了一个基于动态规划的算法。然后，我们提出了一个在线算法，利用李雅普诺夫框架，探索通过能量老化权衡的解决方案空间。大量的实验表明，在线优化框架可以节省超过60%的能量，3倍的收敛速度比以前的计划。
Energy is an essential, but often forgotten aspect in large-scale federated systems. As most of the research focuses on tackling computational and statistical heterogeneity from the machine learning algorithms, the impact on the mobile system still remains unclear. In this paper, we design and implement an online optimization framework by connecting asynchronous execution of federated training with application co-running to minimize energy consumption on battery-powered mobile devices. From a series of experiments, we find that co-running the training process in the background with foreground applications gives the system a deep energy discount with negligible performance slowdown. Based on these results, we first study an offline problem assuming all the future occurrences of applications are available, and propose a dynamic programming-based algorithm. Then we propose an online algorithm using the Lyapunov framework to explore the solution space via the energy-staleness trade-off. The extensive experiments demonstrate that the online optimization framework can save over 60% energy with 3 times faster convergence speed compared to the previous schemes.