In this paper, we consider a decentralized learning problem in the presence of stragglers. Although gradient coding techniques have been developed for distributed learning to evade stragglers, where the devices send encoded gradients with redundant training data, it is difficult to apply those techniques directly to decentralized learning scenarios. To deal with this problem, we propose a new gossip-based decentralized learning method with gradient coding (GOCO). In the proposed method, to avoid the negative impact of stragglers, the parameter vectors are updated locally using encoded gradients based on the framework of stochastic gradient coding and then averaged in a gossip-based manner. We analyze the convergence performance of GOCO for strongly convex loss functions. And we also provide simulation results to demonstrate the superiority of the proposed method in terms of learning performance compared with the baseline methods.

翻译：本文研究了存在掉队者情况下的去中心化学习问题。尽管分布式学习中已开发出梯度编码技术（设备通过冗余训练数据发送编码梯度）来规避掉队者，但这些技术难以直接应用于去中心化学习场景。针对该问题，我们提出了一种新的基于八卦协议的梯度编码去中心化学习方法（GOCO）。该方法中，为消除掉队者的负面影响，参数向量基于随机梯度编码框架使用编码梯度进行局部更新，再通过八卦协议进行平均。我们分析了GOCO在强凸损失函数下的收敛性能，并通过仿真结果证明了所提方法在学习性能上相较于基线方法的优越性。