# Distributed Coordinate Descent for L1-regularized Logistic Regression

arXiv: https://arxiv.org/abs/1411.6520 · HTML: https://mlsanity.com/paper/1411.6520/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Nov 2014.

## Built on by (most cited first, 2 of 2)

- [High-Dimensional Distributed Sparse Classification with Scalable Communication-Efficient Global Updates](https://mlsanity.com/paper/2407.06346/) (2024)
- [Optimizing the Optimal Weighted Average: Efficient Distributed Sparse Classification](https://mlsanity.com/paper/2406.01753/) (2024)

## Abstract (arXiv)

Solving logistic regression with L1-regularization in distributed settings is an important problem. This problem arises when training dataset is very large and cannot fit the memory of a single machine. We present d-GLMNET, a new algorithm solving logistic regression with L1-regularization in the distributed settings. We empirically show that it is superior over distributed online learning via truncated gradient.