Federated edge learning (FEEL) is a widely adopted framework for training an artificial intelligence (AI) model distributively at edge devices to leverage their data while preserving their data privacy. The execution of a power-hungry learning task at energy-constrained devices is a key challenge confronting the implementation of FEEL. To tackle the challenge, we propose the solution of powering devices using wireless power transfer (WPT). To derive guidelines on deploying the resultant wirelessly powered FEEL (WP-FEEL) system, this work aims at the derivation of the tradeoff between the model convergence and the settings of power sources in two scenarios: 1) the transmission power and density of power-beacons (dedicated charging stations) if they are deployed, or otherwise 2) the transmission power of a server (access-point). The development of the proposed analytical framework relates the accuracy of distributed stochastic gradient estimation to the WPT settings, the randomness in both communication and WPT links, and devices' computation capacities. Furthermore, the local-computation at devices (i.e., mini-batch size and processor clock frequency) is optimized to efficiently use the harvested energy for gradient estimation. The resultant learning-WPT tradeoffs reveal the simple scaling laws of the model-convergence rate with respect to the transferred energy as well as the devices' computational energy efficiencies. The results provide useful guidelines on WPT provisioning to provide a guaranteer on learning performance. They are corroborated by experimental results using a real dataset.

中文翻译：

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无线联邦边缘学习：收敛和功率传输之间的最佳权衡

联合边缘学习（FEEL）是一种广泛采用的框架，用于在边缘设备上分布式地训练人工智能（AI）模型，以利用其数据同时保护其数据隐私。在能量受限的设备上执行耗电的学习任务是FEEL实施面临的主要挑战。为了应对这一挑战，我们提出了使用无线电力传输（WPT）为设备供电的解决方案。为了得出有关部署最终的无线FEEL（WP-FEEL）系统的指南，这项工作旨在推导两种情况下模型收敛与电源设置之间的折衷：1）传输功率和功率密度-信标（专用充电站）（如果已部署），否则2）服务器（接入点）的传输功率。所提出的分析框架的发展将分布式随机梯度估计的准确性与WPT设置，通信和WPT链路中的随机性以及设备的计算能力相关联。此外，对设备处的本地计算（即小批量大小和处理器时钟频率）进行了优化，以有效地将采集的能量用于梯度估计。最终的学习-WPT权衡揭示了模型收敛速率相对于传递的能量以及设备的计算能效的简单缩放定律。结果提供了有关WPT设置的有用指导，以确保学习成绩。使用真实数据集的实验结果证实了这些结论。