论文标题
通过应用程序共同运行，在电池供电的移动设备上联合的异步学习的能量最小化
Energy Minimization for Federated Asynchronous Learning on Battery-Powered Mobile Devices via Application Co-running
论文作者
论文摘要
能源是大规模联合系统中的必不可少但经常被遗忘的方面。由于大多数研究重点是从机器学习算法来解决计算和统计异质性，因此对移动系统的影响仍然不清楚。在本文中，我们通过将联合培训的异步执行与应用程序共同运行以最大程度地减少电池供电的移动设备的能源消耗来设计和实施在线优化框架。从一系列实验中，我们发现与前景应用程序中的训练过程共同运行了训练过程，使该系统具有深度的能源折扣，并且性能降低了。基于这些结果，我们首先研究了一个离线问题，假设将来可以使用应用程序，并提出了一种基于动态编程的算法。然后，我们使用Lyapunov框架提出了一种在线算法，以通过能量静止的权衡来探索解决方案空间。广泛的实验表明，与以前的方案相比，在线优化框架可以节省超过60％的能量，而收敛速度的3倍。
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.
