Coresets for Vertical Federated Learning: Regularized Linear Regression and $k$-Means Clustering
Abstract
Vertical federated learning (VFL), where data features are stored in multiple parties distributively, is an important area in machine learning. However, the communication complexity for VFL is typically very high. In this paper, we propose a unified framework by constructing \emph{coresets} in a distributed fashion for communication-efficient VFL. We study two important learning tasks in the VFL setting: regularized linear regression and $k$-means clustering, and apply our coreset framework to both problems. We theoretically show that using coresets can drastically alleviate the communication complexity, while nearly maintain the solution quality. Numerical experiments are conducted to corroborate our theoretical findings.
Cite
Text
Huang et al. "Coresets for Vertical Federated Learning: Regularized Linear Regression and $k$-Means Clustering." Neural Information Processing Systems, 2022.Markdown
[Huang et al. "Coresets for Vertical Federated Learning: Regularized Linear Regression and $k$-Means Clustering." Neural Information Processing Systems, 2022.](https://mlanthology.org/neurips/2022/huang2022neurips-coresets-a/)BibTeX
@inproceedings{huang2022neurips-coresets-a,
title = {{Coresets for Vertical Federated Learning: Regularized Linear Regression and $k$-Means Clustering}},
author = {Huang, Lingxiao and Li, Zhize and Sun, Jialin and Zhao, Haoyu},
booktitle = {Neural Information Processing Systems},
year = {2022},
url = {https://mlanthology.org/neurips/2022/huang2022neurips-coresets-a/}
}