Federated edge learning (FEL) can training a global model from terminal nodes' local dataset, which can make full use of the computing resources of terminal nodes and performs more extensive and efficient machine learning on terminal nodes with protecting user information requirements. Performance of FEL will be suffered from long delay or fault decision as the master collects partial gradients from stragglers which cannot return correct results within a deadline. Inspired by this, in this paper, we propose a novel coded FEL to mitigate stragglers for synchronous gradient with a two-stage dynamic scheme, where we start with part of workers for a duration of before starting the second stage, and on completion of at the first stage, we start remaining workers in the second stage. In particular, the computation latency and transmission latency is essential and should be quantitatively analyzed. Then the dynamically coded coefficients scheme is proposed which is based on historical information including worker completion time. For performance optimization of FEL, a Lyapunov function is designed to maximize admission data balancing fairness and two stage dynamic coding scheme is designed to maximize arrival data among workers. Experimental evidence verifies the derived properties and demonstrates that our proposed solution achieves a better performance for practical network parameters and benchmark datasets in terms of accuracy and resource utilization in the FEL system.
翻译:联邦边缘学习(FEL)可以培训来自终端节点当地数据集的全球模型,该模型可以充分利用终端节点的计算资源,并在终端节点上进行更广泛和高效的机器学习,保护用户信息要求; FEL的绩效将长期拖延或错误决定,因为船长收集来自straglers的部分梯度,无法在最后期限内返回正确的结果。我们在此文件中提议了一个新型编码FEL,以减少同步梯度的分层梯度,采用两阶段动态计划,在第二阶段开始之前从部分工人开始,在第一阶段完成时,我们开始在终端节点上进行更广泛和高效的机器学习;特别是,计算延时和传输延度必须进行定量分析。之后,根据历史信息,包括工人的完成时间,提出动态编码系数计划。关于FEL的绩效优化,Lyapunov功能旨在最大限度地实现接收数据平衡公平性,而两个阶段动态编码计划的设计是要在第二阶段开始之前从部分工人开始,在第二阶段完成时开始,我们开始在第二阶段开始就剩余工人开始。特别是,计算延后,计算延迟和传输时间是关键分析。然后,然后根据历史信息,根据包括工人完成时间的历史信息,测试测试数据,检验我们的拟议基准参数,测试数据,以便实现我们的拟议数据使用。