Communication-Efficient Distributed Dual Coordinate Ascent
This addresses the communication overhead problem for distributed machine learning practitioners, offering a significant speedup but is incremental as it builds on existing primal-dual methods.
The paper tackles the communication bottleneck in distributed optimization for large-scale machine learning by proposing CoCoA, a communication-efficient framework using local computation in a primal-dual setting, which converges 25x faster than state-of-the-art methods to a .001-accurate solution.
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.