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.

Authors

• Martin Jaggi EPFL
• Virginia Smith
• Martin Takac
• Jonathan Terhorst UC Berkeley
• Sanjay Krishnan
• Thomas Hofmann ETH Zürich
• Michael Jordan

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