# LOCO: Distributing Ridge Regression with Random Projections

arXiv: https://arxiv.org/abs/1406.3469 · HTML: https://mlsanity.com/paper/1406.3469/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jun 2014.

## Built on by (most cited first, 3 of 3)

- [Federated Learning of Models Pre-Trained on Different Features with Consensus Graphs](https://mlsanity.com/paper/2306.01240/) (2023)
- [Distributed Sparse Linear Regression under Communication Constraints](https://mlsanity.com/paper/2301.04022/) (2023)
- [Distributed Continual Learning with CoCoA in High-dimensional Linear Regression](https://mlsanity.com/paper/2312.01795/) (2023)

## Abstract (arXiv)

We propose LOCO, an algorithm for large-scale ridge regression which distributes the features across workers on a cluster. Important dependencies between variables are preserved using structured random projections which are cheap to compute and must only be communicated once. We show that LOCO obtains a solution which is close to the exact ridge regression solution in the fixed design setting. We verify this experimentally in a simulation study as well as an application to climate prediction. Furthermore, we show that LOCO achieves significant speedups compared with a state-of-the-art distributed algorithm on a large-scale regression problem.