Communication-Censored Distributed Stochastic Gradient Descent
This addresses communication bottlenecks in distributed machine learning applications, offering an incremental improvement over existing methods like quantization and sparsification.
The paper tackles the problem of high communication costs in distributed stochastic optimization by introducing a communication-censoring technique that transmits gradients only when they are sufficiently informative, reusing stale ones otherwise, and achieves the same convergence rate as SGD while significantly reducing communication, with numerical experiments showing sizable savings.
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.