  To accelerate the training of machine learning models, distributed stochastic
gradient descent (SGD) and its variants have been widely adopted, which apply
multiple workers in parallel to speed up training. Among them, Local SGD has
gained much attention due to its lower communication cost. Nevertheless, when
the data distribution on workers is non-identical, Local SGD requires
$O(T^{\frac{3}{4}} N^{\frac{3}{4}})$ communications to maintain its
\emph{linear iteration speedup} property, where $T$ is the total number of
iterations and $N$ is the number of workers. In this paper, we propose Variance
Reduced Local SGD (VRL-SGD) to further reduce the communication complexity.
Benefiting from eliminating the dependency on the gradient variance among
workers, we theoretically prove that VRL-SGD achieves a \emph{linear iteration
speedup} with a lower communication complexity $O(T^{\frac{1}{2}}
N^{\frac{3}{2}})$ even if workers access non-identical datasets. We conduct
experiments on three machine learning tasks, and the experimental results
demonstrate that VRL-SGD performs impressively better than Local SGD when the
data among workers are quite diverse.

Machine Learning;Distributed, Parallel, and Cluster Computing;Optimization and Control;Machine Learning