Title: Label-shift robust federated feature screening for high-dimensional classification Show Chinese title

Title: 类别分布偏移鲁棒的联邦高维分类特征筛选方法

Abstract: Distributed and federated learning are important tools for high-dimensional classification of large datasets. To reduce computational costs and overcome the curse of dimensionality, feature screening plays a pivotal role in eliminating irrelevant features during data preprocessing. However, data heterogeneity, particularly label shifting across different clients, presents significant challenges for feature screening. This paper introduces a general framework that unifies existing screening methods and proposes a novel utility, label-shift robust federated feature screening (LR-FFS), along with its federated estimation procedure. The framework facilitates a uniform analysis of methods and systematically characterizes their behaviors under label shift conditions. Building upon this framework, LR-FFS leverages conditional distribution functions and expectations to address label shift without adding computational burdens and remains robust against model misspecification and outliers. Additionally, the federated procedure ensures computational efficiency and privacy protection while maintaining screening effectiveness comparable to centralized processing. We also provide a false discovery rate (FDR) control method for federated feature screening. Experimental results and theoretical analyses demonstrate LR-FFS's superior performance across diverse client environments, including those with varying class distributions, sample sizes, and missing categorical data. Show Chinese abstract

Abstract: 分布式学习和联邦学习是处理大规模数据集高维分类的重要工具。为了降低计算成本并克服维度灾难，特征筛选在数据预处理阶段扮演着关键角色，用于消除无关特征。然而，数据异质性，特别是不同客户端之间标签偏移带来的挑战，对特征筛选提出了重大难题。 本文提出了一种统一现有筛选方法的通用框架，并引入了一种新颖的实用工具：标签偏移鲁棒联邦特征筛选（LR-FFS）及其对应的联邦估计程序。该框架能够统一分析各种方法，并系统地描述它们在标签偏移条件下的行为。基于此框架，LR-FFS 利用条件分布函数和期望来解决标签偏移问题，同时不会增加额外的计算负担，并且对模型误设和异常值具有鲁棒性。此外，联邦程序确保了计算效率和隐私保护，同时保持与集中式处理相当的筛选效果。 我们还为联邦特征筛选提供了一种错误发现率（FDR）控制方法。实验结果和理论分析表明，LR-FFS 在各种客户环境中的表现优异，包括具有不同类别分布、样本大小以及缺失分类数据的情况。