Communication-efficient sparse regression: a one-shot approach

Explosive growth in the size of modern datasets has fueled interest in distributed statistical learning. For examples, we refer to Boyd et al. (2011); Dekel et al. (2012); Duchi, Agarwal and Wainwright (2012); Zhang, Duchi and Wainwright (2013) and the references therein. The problem arises, for example, when working with datasets that are too large to fit on a single machine and must be distributed across multiple machines. The main bottleneck in the distributed setting is usually communication between machines/processors, so the overarching goal of algorithm design is to minimize communication costs.