Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
12-2022
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 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.
Discipline
Databases and Information Systems
Research Areas
Data Science and Engineering; Intelligent Systems and Optimization
Publication
Proceedings of the 36th Conference on Neural Information Processing Systems (NeurIPS 2022), Hybrid Conference, November 28 - December 9
First Page
1
Last Page
32
Publisher
Neural Information Processing Systems Foundation
City or Country
New Orleans, USA
Citation
HUANG, Lingxiao; LI, Zhize; SUN, Jialin; and ZHAO, Haoyu.
Coresets for vertical federated learning: Regularized linear regression and k-means clustering. (2022). Proceedings of the 36th Conference on Neural Information Processing Systems (NeurIPS 2022), Hybrid Conference, November 28 - December 9. 1-32.
Available at: https://ink.library.smu.edu.sg/sis_research/8686
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://proceedings.neurips.cc/paper_files/paper/2022/hash/be7b70477c8fca697f14b1dbb1c086d1-Abstract-Conference.html