Abstract

Communication remains the most significant bottleneck in the performance of distributed optimization algorithms for large-scale machine learning. In this paper, we propose a communication-efficient framework, C O C OA, that uses local computation in a primal-dual setting to dramatically reduce the amount of necessary communication. We provide a strong convergence rate analysis for this class of algorithms, as well as experiments on real-world distributed datasets with implementations in Spark. In our experiments, we find that as compared to stateof-the-art mini-batch versions of SGD and SDCA algorithms, C O C OA converges to the same .001-accurate solution quality on average 25× as quickly.