Coresets for Vertical Federated Learning: Regularized Linear Regression and $K$-Means Clustering
Lingxiao Huang, Zhize Li, Jialin Sun, Haoyu Zhao
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.
BibTeX
@inproceedings{
huang2022coresets,
title={Coresets for Vertical Federated Learning: Regularized Linear Regression and \$K\$-Means Clustering},
author={Lingxiao Huang and Zhize Li and Jialin Sun and Haoyu Zhao},
booktitle={Advances in Neural Information Processing Systems},
editor={Alice H. Oh and Alekh Agarwal and Danielle Belgrave and Kyunghyun Cho},
year={2022},
url={https://openreview.net/forum?id=N0tKCpMhA2}
}