论文标题
用于垂直联合学习的核心：正规化线性回归和$ k $ -MEANS聚类
Coresets for Vertical Federated Learning: Regularized Linear Regression and $K$-Means Clustering
论文作者
论文摘要
垂直联合学习（VFL），数据特征在多个方面存储在分布式中，是机器学习的重要领域。但是，VFL的通信复杂性通常很高。在本文中，我们通过以分布式的方式构建核心框架来提出一个统一的框架。我们在VFL设置中研究了两个重要的学习任务：正规化的线性回归和$ K $ -MEANS聚类，并将我们的核心框架应用于两个问题。从理论上讲，我们表明，使用核心可以大大减轻沟通的复杂性，同时几乎保持解决方案质量。进行数值实验以证实我们的理论发现。
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.
