High-Dimensional Distributed Sparse Classification with Scalable Communication-Efficient Global Updates
This addresses communication bottlenecks in distributed machine learning for large-scale datasets, though it is incremental as it builds on existing surrogate likelihood methods.
The paper tackles the problem of communication costs in distributed training of high-dimensional sparse logistic regression models, achieving a large improvement in accuracy with only a few update steps and similar or faster runtimes, even beyond millions of features.
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}.