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, COCOA, 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 state-of-the-art mini-batch versions of SGD and SDCA algorithms, COCOA converges to the same .001-accurate solution quality on average 25 × as quickly.
['Martin Jaggi', 'Virginia Smith', 'Martin Takáč', 'Jonathan Terhorst', 'Sanjay Krishnan', 'Thomas Hofmann', 'Michael I. Jordan']
Communication-Efficient Distributed Dual Coordinate Ascent