计算机应用研究2026，Vol.43Issue(5)：1292-1301,10.DOI:10.19734/j.issn.1001-3695.2025.10.0397
联邦学习的计算效率优化方法综述
Survey on computational efficiency optimization methods for federated learning
摘要
Abstract
Federated learning,as a distributed machine learning paradigm,enables collaborative model training with privacy preservation through parameter aggregation mechanisms,demonstrating potential in addressing data silo issues in fields such as smart healthcare and the Internet of Things.However,computational efficiency bottlenecks in resource-constrained devices se-verely hinder its practical deployment.Compared to traditional centralized learning paradigms,federated learning still faces challenges such as high communication overhead and insufficient adaptability to dynamic environments while protecting data privacy.This paper systematically reviewed efficiency enhancement methods for federated learning from three major directions:model compression,update strategy optimization,and data computation optimization.Existing studies show that collaborative optimization of compression strategies and dynamic resource allocation reduces communication overhead and accelerates conver-gence.Nevertheless,current approaches still encounter challenges including model accuracy loss and inadequate adaptability to dynamic environments.Finally,it proposed future research directions to explore lightweight native model architectures,in-telligent dynamic update mechanisms,and efficient malicious detection methods,aiming to balance efficiency and security for promoting the large-scale application of federated learning in complex scenarios.关键词
联邦学习/计算效率/资源受限/模型压缩/更新策略/数据优化Key words
federated learning/computational efficiency/resource constraints/model compression/update strategies/data optimization分类
信息技术与安全科学引用本文复制引用
董华,范菁,郗恩康,金亚东,俞浩,孙伊航..联邦学习的计算效率优化方法综述[J].计算机应用研究,2026,43(5):1292-1301,10.基金项目
国家自然科学基金资助项目(12361104) （12361104）
教育部-新一代信息技术创新项目(2023IT077) （2023IT077）
云南省教育厅科学研究基金资助项目(2025Y0670) （2025Y0670）
云南省吴中海专家工作站资助项目(202305AF150045) （202305AF150045）
云南省教育厅科学研究基金资助项目(2023Y0499) （2023Y0499）
CCF-深信服"远望"科研基金资助项目(20240210) （20240210）
云南省教育厅科学研究基金资助项目(2025Y0670) （2025Y0670）
