The scale of modern datasets necessitates the development of efficient distributed optimization methods for machine learning. We present a general-purpose framework for distributed computing environments, CoCoA, that has an efficient communication scheme and is applicable to a wide variety of problems in machine learning and signal processing. We extend the framework to cover general non-strongly-convex regularizers, including L1-regularized problems like lasso, sparse logistic regression, and elastic net regularization, and show how earlier work can be derived as a special case. We provide convergence guarantees for the class of convex regularized loss minimization objectives, leveraging a novel approach in handling non-strongly-convex regularizers and non-smooth loss functions. The resulting framework has markedly improved performance over state-of-the-art methods, as we illustrate with an extensive set of experiments on real distributed datasets.
@article{arxiv.1611.02189,
title = {CoCoA: A General Framework for Communication-Efficient Distributed Optimization},
author = {Virginia Smith and Simone Forte and Chenxin Ma and Martin Takac and Michael I. Jordan and Martin Jaggi},
journal= {arXiv preprint arXiv:1611.02189},
year = {2018}
}