The continuous convergence of machine learning algorithms, 5G and beyond (5G+) wireless communications, and artificial intelligence (AI) hardware implementation hastens the birth of federated learning (FL) over 5G+ mobile devices, which pushes AI functions to mobile devices and initiates a new era of on-device AI applications. Despite the remarkable progress made in FL, huge energy consumption is one of the most significant obstacles restricting the development of FL over battery-constrained 5G+ mobile devices. To address this issue, in this paper, we investigate how to develop energy efficient FL over 5G+ mobile devices by making a trade-off between energy consumption for "working" (i.e., local computing) and that for "talking" (i.e., wireless communications) in order to boost the overall energy efficiency. Specifically, we first examine energy consumption models for graphics processing unit (GPU) computation and wireless transmissions. Then, we overview the state of the art of integrating FL procedure with energy-efficient learning techniques (e.g., gradient sparsification, weight quantization, pruning, etc.). Finally, we present several potential future research directions for FL over 5G+ mobile devices from the perspective of energy efficiency.
翻译:机器学习算法、5G及以后(5G+)无线通信和人工智能硬件的不断趋同(5G+)无线通信和人工智能硬件的实施加速了5G+移动设备的联合学习(FL)的诞生,从而将AI功能推向移动设备,并开启了安装AI应用程序的新时代。尽管在FL取得了显著进展,但巨大的能源消耗是限制FL对受电池限制的5G+移动设备开发FL的最大障碍之一。为了解决这一问题,我们在本文件中调查了如何通过“工作”(即本地计算)和“谈话”(无线通信)能源消耗之间的交换来开发5G+移动设备,从而在“工作”(即本地计算)和“谈话”(即无线通信)之间开发节能FL,以便提高总体能源效率。具体地说,我们首先研究图形处理单位(GPU)计算和无线传输的能源消费模式。然后,我们回顾将FL程序与节能学习技术(例如梯式喷雾化、重量平整、裁剪裁等)相结合的工艺的现状。最后,我们从未来对5G的能源效率进行了若干研究。