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Stochastic gradient descent (SGD) has taken the stage as the primaryworkhorse for large-scale machine learning. It is often used with its adaptivevariants such as AdaGrad, Adam, and AMSGrad. This paper proposes an adaptivestochastic gradient descent method for distributed machine learning, which canbe viewed as the communication-adaptive counterpart of the celebrated Adammethod - justifying its name CADA. The key components of CADA are a set of newrules tailored for adaptive stochastic gradients that can be implemented tosave communication upload. The new algorithms adaptively reuse the stale Adamgradients, thus saving communication, and still have convergence ratescomparable to original Adam. In numerical experiments, CADA achieves impressiveempirical performance in terms of total communication round reduction.

CADA: Communication-Adaptive Distributed Adam