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Stochastic Gradient Descent (SGD) has become one of the most popularoptimization methods for training machine learning models on massive datasets.However, SGD suffers from two main drawbacks: (i) The noisy gradient updateshave high variance, which slows down convergence as the iterates approach theoptimum, and (ii) SGD scales poorly in distributed settings, typicallyexperiencing rapidly decreasing marginal benefits as the number of workersincreases. In this paper, we propose a highly parallel method, CentralVR, thatuses error corrections to reduce the variance of SGD gradient updates, andscales linearly with the number of worker nodes. CentralVR enjoys low iterationcomplexity, provably linear convergence rates, and exhibits linear performancegains up to hundreds of cores for massive datasets. We compare CentralVR tostate-of-the-art parallel stochastic optimization methods on a variety ofmodels and datasets, and find that our proposed methods exhibit strongerscaling than other SGD variants.

Efficient Distributed SGD with Variance Reduction