Catering to the proliferation of Internet of Things devices and distributed machine learning at the edge, we propose an energy harvesting federated learning (EHFL) framework in this paper. The introduction of EH implies that a client's availability to participate in any FL round cannot be guaranteed, which complicates the theoretical analysis. We derive novel convergence bounds that capture the impact of time-varying device availabilities due to the random EH characteristics of the participating clients, for both parallel and local stochastic gradient descent (SGD) with non-convex loss functions. The results suggest that having a uniform client scheduling that maximizes the minimum number of clients throughout the FL process is desirable, which is further corroborated by the numerical experiments using a real-world FL task and a state-of-the-art EH scheduler.
翻译:我们提议在本文中建立一个节能联合学习框架(EHFL),这意味着无法保证客户能够参加任何FL回合,这就使理论分析复杂化。我们得出新的趋同界限,从参与的客户随机的EH特性中,可以捕捉时间变化装置的可用性的影响,包括平行的和局部的随机弹性梯度下降(SGD)以及非凝聚损失功能。结果显示,统一客户时间安排,使FL过程的最小客户人数最大化,是可取的,而使用现实世界FL任务和最先进的EH排程进行的数字实验进一步证实了这一点。