Communication-Efficient Distributed Dual Coordinate Ascent
  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 25x as quickly. 
Martin Jaggi, Virginia Smith, Martin Tak\'a\v{c}, Jonathan Terhorst,   Sanjay Krishnan, Thomas Hofmann, Michael I. Jordan
cs.LG math.OC stat.ML