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节点情况下的分布式学习问题。尽管梯度编码技术已被开发用于分布式学习以规避掉队节点（其中设备利用冗余训练数据发送编码梯度），但这些技术难以直接应用于分布式学习场景。针对该问题，我们提出了一种基于 gossip 协议的新型分布式学习方法——GOCO（Gossip-based gradient coding）。该方法基于随机梯度编码框架，通过编码梯度局部更新参数向量，并采用 gossip 协议进行平均，从而避免掉队节点的负面影响。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该方法相较基线方法在学习性能上的优越性。