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 25× as quickly.

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Primal-Dual Parallel Coordinate Descent for Machine Learning Optimization

  • Martin Takáč, Peter Richtárik, Nathan Srebro
  • 2014
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