聊城大学学报（自然科学版）2025，Vol.38Issue(4)：497-506,10.DOI:10.19728/j.issn1672-6634.2024070004
隐私保护下多方高维数据联邦特征选择算法
Federated feature selection algorithm for multi-party high-dimensional data under privacy protection
摘要
Abstract
High-dimensional feature selection faces challenges such as the"curse of dimensionality"and high computational costs.Due to privacy protection constraints,a large amount of high-dimensional data may be distributed and stored across different institutions(referred to as participants)and cannot be shared,further complicating the joint feature selection of multi-party high-dimensional data.In light of this,this paper proposes a surrogate-joint-assisted federated evolutionary feature selection algorithm to ad-dress the issue of high-dimensional feature selection involving multiple participants under privacy protec-tion.A framework for a surrogate-assisted federated evolutionary feature selection algorithm is designed,and based on this framework,strategies for joint construction and management of surrogate models,joint evaluation based on surrogate models,and joint updating of individuals are provided.Finally,the proposed algorithm is applied to 10 test datasets and compared with 3 typical wrapper-based evolutionary feature se-lection algorithms.The results show that the proposed algorithm not only ensures the classification per-formance of the algorithm while fully protecting the data privacy of the participants but also significantly improves the algorithm's runtime.关键词
特征选择/进化算法/代理辅助/隐私保护Key words
feature selection/evolutionary algorithm/surrogate-assisted/privacy protection分类
信息技术与安全科学引用本文复制引用
金鑫,胡滢,李芃,郑明..隐私保护下多方高维数据联邦特征选择算法[J].聊城大学学报（自然科学版）,2025,38(4):497-506,10.基金项目
国家自然科学基金项目(62306009) （62306009）
安徽省自然科学基金项目(2308085QF209)资助 （2308085QF209）
