# Robust Coreset Construction for Distributed Machine Learning

arXiv: https://arxiv.org/abs/1904.05961 · HTML: https://mlsanity.com/paper/1904.05961/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Apr 2019.

## Built on by (most cited first, 6 of 6)

- [Performance Scaling via Optimal Transport: Enabling Data Selection from Partially Revealed Sources](https://mlsanity.com/paper/2307.02460/) (2023)
- [Coresets for Vertical Federated Learning: Regularized Linear Regression and K-Means Clustering](https://mlsanity.com/paper/2210.14664/) (2022)
- [Coresets for Decision Trees of Signals](https://mlsanity.com/paper/2110.03195/) (2021)
- [Active Learning for WBAN-based Health Monitoring](https://mlsanity.com/paper/2408.02849/) (2024)
- [Communication-efficient k-Means for Edge-based Machine Learning](https://mlsanity.com/paper/2102.04282/) (2021)
- [Joint Coreset Construction and Quantization for Distributed Machine Learning](https://mlsanity.com/paper/2204.06652/) (2022)

## Abstract (arXiv)

Coreset, which is a summary of the original dataset in the form of a small weighted set in the same sample space, provides a promising approach to enable machine learning over distributed data. Although viewed as a proxy of the original dataset, each coreset is only designed to approximate the cost function of a specific machine learning problem, and thus different coresets are often required to solve different machine learning problems, increasing the communication overhead. We resolve this dilemma by developing robust coreset construction algorithms that can support a variety of machine learning problems. Motivated by empirical evidence that suitably-weighted k-clustering centers provide a robust coreset, we harden the observation by establishing theoretical conditions under which the coreset provides a guaranteed approximation for a broad range of machine learning problems, and developing both centralized and distributed algorithms to generate coresets satisfying the conditions. The robustness of the proposed algorithms is verified through extensive experiments on diverse datasets with respect to both supervised and unsupervised learning problems.