High-Dimensional Distributed Sparse Classification with Scalable Communication-Efficient Global Updates

TL;DR

This paper introduces a scalable, communication-efficient distributed method for high-dimensional sparse logistic regression, effectively handling massive datasets with millions of features and improving accuracy with minimal communication rounds.

Contribution

It develops novel solutions to address divergence and sparsity challenges in distributed surrogate likelihood optimization for logistic regression on large-scale data.

Findings

Significant accuracy improvement over existing distributed algorithms

Fewer communication rounds needed for convergence

Comparable or faster runtimes on large datasets

Abstract

As the size of datasets used in statistical learning continues to grow, distributed training of models has attracted increasing attention. These methods partition the data and exploit parallelism to reduce memory and runtime, but suffer increasingly from communication costs as the data size or the number of iterations grows. Recent work on linear models has shown that a surrogate likelihood can be optimized locally to iteratively improve on an initial solution in a communication-efficient manner. However, existing versions of these methods experience multiple shortcomings as the data size becomes massive, including diverging updates and efficiently handling sparsity. In this work we develop solutions to these problems which enable us to learn a communication-efficient distributed logistic regression model even beyond millions of features. In our experiments we demonstrate a large improvement in accuracy over distributed algorithms with only a few distributed update steps needed, and similar or faster runtimes. Our code is available at \url{https://github.com/FutureComputing4AI/ProxCSL}.