  This paper develops a communication-efficient algorithm to solve the
stochastic optimization problem defined over a distributed network, aiming at
reducing the burdensome communication in applications such as distributed
machine learning.Different from the existing works based on quantization and
sparsification, we introduce a communication-censoring technique to reduce the
transmissions of variables, which leads to our communication-Censored
distributed Stochastic Gradient Descent (CSGD) algorithm. Specifically, in
CSGD, the latest mini-batch stochastic gradient at a worker will be transmitted
to the server if and only if it is sufficiently informative. When the latest
gradient is not available, the stale one will be reused at the server. To
implement this communication-censoring strategy, the batch-size is increasing
in order to alleviate the effect of stochastic gradient noise. Theoretically,
CSGD enjoys the same order of convergence rate as that of SGD, but effectively
reduces communication. Numerical experiments demonstrate the sizable
communication saving of CSGD.
