Coresets for vertical federated learning: Regularized linear regression and k-means clustering

Coresets for vertical federated learning: Regularized linear regression and k-means clustering

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 coresets in a distribute...

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Main Authors: HUANG, Lingxiao, LI, Zhize, SUN, Jialin, ZHAO, Haoyu
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2022
Subjects:
Databases and Information Systems
Online Access:https://ink.library.smu.edu.sg/sis_research/8686
https://ink.library.smu.edu.sg/context/sis_research/article/9689/viewcontent/NeurIPS22_full_coresetvfl.pdf
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spelling sg-smu-ink.sis_research-96892024-03-28T08:46:28Z Coresets for vertical federated learning: Regularized linear regression and k-means clustering HUANG, Lingxiao LI, Zhize SUN, Jialin ZHAO, Haoyu 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 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. 2022-12-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/8686 https://ink.library.smu.edu.sg/context/sis_research/article/9689/viewcontent/NeurIPS22_full_coresetvfl.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Databases and Information Systems
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Databases and Information Systems
spellingShingle Databases and Information Systems
HUANG, Lingxiao
LI, Zhize
SUN, Jialin
ZHAO, Haoyu
Coresets for vertical federated learning: Regularized linear regression and k-means clustering
description 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 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.
format text
author HUANG, Lingxiao
LI, Zhize
SUN, Jialin
ZHAO, Haoyu
author_facet HUANG, Lingxiao
LI, Zhize
SUN, Jialin
ZHAO, Haoyu
author_sort HUANG, Lingxiao
title Coresets for vertical federated learning: Regularized linear regression and k-means clustering
title_short Coresets for vertical federated learning: Regularized linear regression and k-means clustering
title_full Coresets for vertical federated learning: Regularized linear regression and k-means clustering
title_fullStr Coresets for vertical federated learning: Regularized linear regression and k-means clustering
title_full_unstemmed Coresets for vertical federated learning: Regularized linear regression and k-means clustering
title_sort coresets for vertical federated learning: regularized linear regression and k-means clustering
publisher Institutional Knowledge at Singapore Management University
publishDate 2022
url https://ink.library.smu.edu.sg/sis_research/8686
https://ink.library.smu.edu.sg/context/sis_research/article/9689/viewcontent/NeurIPS22_full_coresetvfl.pdf
_version_ 1795302172799270912

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