We consider distributed gradient descent in the presence of stragglers.
Recent work on \em gradient coding \em and \em approximate gradient coding \em
have shown how to add redundancy in distributed gradient descent to guarantee
convergence even if some workers are \em stragglers\em---that is, slow or
non-responsive. In this work we propose an approximate gradient coding scheme
called \em Stochastic Gradient Coding \em (SGC), which works when the
stragglers are random. SGC distributes data points redundantly to workers
according to a pair-wise balanced design, and then simply ignores the
stragglers. We prove that the convergence rate of SGC mirrors that of batched
Stochastic Gradient Descent (SGD) for the $\ell_2$ loss function, and show how
the convergence rate can improve with the redundancy. We also provide bounds
for more general convex loss functions. We show empirically that SGC requires a
small amount of redundancy to handle a large number of stragglers and that it
can outperform existing approximate gradient codes when the number of
stragglers is large.

本文提出了一种适用于随机拖延者的梯度编码方案（SGC），它通过一种成对平衡的设计将数据点冗余分配给工作人员，并忽略了拖延者，证明了 SGC 的收敛速度与批量随机梯度下降的收敛速度相匹配，并演示了设计中的冗余量如何改进收敛速度和处理大量拖延者。