通信学报2025，Vol.46Issue(1)：79-92,14.DOI:10.11959/j.issn.1000-436x.2025008
基于零集中差分隐私的联邦学习激励方案
Incentive scheme for federated learning based on zero-concentrated differential privacy
摘要
Abstract
To solve problems of unfair client selection and inefficient model training in federated learning,a privacy-preserving federated learning framework was proposed based on the incentive mechanism named zCDP-FL.An incen-tive mechanism algorithm,SRAI,was designed to maximize system benefits by applying the second price and the re-verse auction to the client's selection strategy.In addition,a dynamic allocation algorithm for the privacy budget was proposed based on the zero-concentrated differential privacy to realize the dynamic adjustment of noise scale during the training,which provided a stronger privacy guarantee under the strict privacy constraint.Theoretical analyses and simula-tion experiments demonstrate that zCDP-FL can effectively prevent privacy leakage and enhance 2.13%～3.62%model training efficiency.关键词
联邦学习/零集中差分隐私/激励机制/隐私预算/动态分配Key words
federated learning/zero-concentrated differential privacy/incentive mechanism/privacy budget/dynamic al-location分类
电子信息工程引用本文复制引用
李梦倩,田有亮,张军鹏,赵冬梅..基于零集中差分隐私的联邦学习激励方案[J].通信学报,2025,46(1):79-92,14.基金项目
国家自然科学基金资助项目(No.62272123,No.61672206,No.62062020) （No.62272123,No.61672206,No.62062020）
中央引导地方科技发展基金资助项目(No.236Z0104G) （No.236Z0104G）
河北省科技计划基金资助项目(No.22567606H) （No.22567606H）
贵州省高层次创新型人才基金资助项目(No.[2020]6008) （No.[2020]6008）
贵州省科技计划基金资助项目(No.[2020]5017,No.[2022]065) The National Natural Science Foundation of China(No.62272123,No.61672206,No.62062020),Central Gov-ernment Guides Local Science and Technology Development Found Projects(No.236Z0104G),The Science and Technology Pro-gram of Hebei Province(No.22567606H),The Project of High-Level Innovative Talents of Guizhou Province(No.[2020]6008),The Science and Technology Program of Guizhou Province(No.[2020]5017,No.[2022]065) （No.[2020]5017,No.[2022]065）
