# Optimizing the Optimal Weighted Average: Efficient Distributed Sparse Classification

arXiv: https://arxiv.org/abs/2406.01753 · HTML: https://mlsanity.com/paper/2406.01753/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 41 references that are in the map.
- First posted on arXiv 3 Jun 2024.

## Builds on (most cited first, 10 of 10)

- [EMBER: An Open Dataset for Training Static PE Malware Machine Learning Models](https://mlsanity.com/paper/1804.04637/) (2018)
- [Communication Efficient Distributed Optimization using an Approximate Newton-type Method](https://mlsanity.com/paper/1312.7853/) (2013)
- [CoCoA: A General Framework for Communication-Efficient Distributed Optimization](https://mlsanity.com/paper/1611.02189/) (2016)
- [Efficient Distributed Learning with Sparsity](https://mlsanity.com/paper/1605.07991/) (2016)
- [Communication-Efficient Distributed Dual Coordinate Ascent](https://mlsanity.com/paper/1409.1458/) (2014)
- [Distributed Coordinate Descent Method for Learning with Big Data](https://mlsanity.com/paper/1310.2059/) (2013)
- [On the Optimality of Averaging in Distributed Statistical Learning](https://mlsanity.com/paper/1407.2724/) (2014)
- [The ensmallen library for flexible numerical optimization](https://mlsanity.com/paper/2108.12981/) (2021)
- [L1-Regularized Distributed Optimization: A Communication-Efficient Primal-Dual Framework](https://mlsanity.com/paper/1512.04011/) (2015)
- [Distributed Coordinate Descent for L1-regularized Logistic Regression](https://mlsanity.com/paper/1411.6520/) (2014)

## Built on by (most cited first, 1 of 1)

- [Simpler Methods Work Better for L1 Penalized Logistic Models and Large Datasets](https://mlsanity.com/paper/2609.23995/) (2026)

## Abstract (arXiv)

While distributed training is often viewed as a solution to optimizing linear models on increasingly large datasets, inter-machine communication costs of popular distributed approaches can dominate as data dimensionality increases. Recent work on non-interactive algorithms shows that approximate solutions for linear models can be obtained efficiently with only a single round of communication among machines. However, this approximation often degenerates as the number of machines increases. In this paper, building on the recent optimal weighted average method, we introduce a new technique, ACOWA, that allows an extra round of communication to achieve noticeably better approximation quality with minor runtime increases. Results show that for sparse distributed logistic regression, ACOWA obtains solutions that are more faithful to the empirical risk minimizer and attain substantially higher accuracy than other distributed algorithms.