  Distributed stochastic gradient descent (SGD) is essential for scaling the
machine learning algorithms to a large number of computing nodes. However, the
infrastructures variability such as high communication delay or random node
slowdown greatly impedes the performance of distributed SGD algorithm,
especially in a wireless system or sensor networks. In this paper, we propose
an algorithmic approach named Overlap-Local-SGD (and its momentum variant) to
overlap the communication and computation so as to speedup the distributed
training procedure. The approach can help to mitigate the straggler effects as
well. We achieve this by adding an anchor model on each node. After multiple
local updates, locally trained models will be pulled back towards the
synchronized anchor model rather than communicating with others. Experimental
results of training a deep neural network on CIFAR-10 dataset demonstrate the
effectiveness of Overlap-Local-SGD. We also provide a convergence guarantee for
the proposed algorithm under non-convex objective functions.
