# DUAL-LOCO: Distributing Statistical Estimation Using Random Projections

arXiv: https://arxiv.org/abs/1506.02554 · HTML: https://mlsanity.com/paper/1506.02554/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2015.

## Built on by (most cited first, 7 of 7)

- [An Introduction to Johnson-Lindenstrauss Transforms](https://mlsanity.com/paper/2103.00564/) (2021)
- [Federated Learning of Models Pre-Trained on Different Features with Consensus Graphs](https://mlsanity.com/paper/2306.01240/) (2023)
- [Statistical optimality conditions for compressive ensembles](https://mlsanity.com/paper/2106.01092/) (2021)
- [One-Shot Federated Ridge Regression: Exact Recovery via Sufficient Statistic Aggregation](https://mlsanity.com/paper/2601.08216/) (2026)
- [Distributed Sparse Linear Regression under Communication Constraints](https://mlsanity.com/paper/2301.04022/) (2023)
- [Scalable High-Dimensional Multivariate Linear Regression for Feature-Distributed Data](https://mlsanity.com/paper/2307.03410/) (2023)
- [Distributed Continual Learning with CoCoA in High-dimensional Linear Regression](https://mlsanity.com/paper/2312.01795/) (2023)

## Abstract (arXiv)

We present DUAL-LOCO, a communication-efficient algorithm for distributed statistical estimation. DUAL-LOCO assumes that the data is distributed according to the features rather than the samples. It requires only a single round of communication where low-dimensional random projections are used to approximate the dependences between features available to different workers. We show that DUAL-LOCO has bounded approximation error which only depends weakly on the number of workers. We compare DUAL-LOCO against a state-of-the-art distributed optimization method on a variety of real world datasets and show that it obtains better speedups while retaining good accuracy.