计算机工程与应用2024，Vol.60Issue(19)：278-287,10.DOI:10.3778/j.issn.1002-8331.2306-0366
面向多源数据的个性化联邦学习框架
Personalized Federal Learning Framework for Multi-Source Data
摘要
Abstract
In federated learning,the central server aggregates and models from different clients after differential privacy perturbation,in which the size of differential privacy noise addition and the allocation of the privacy budget directly affect the usability of the model,most of the existing studies are based on balanced data and fixed privacy budgets,which makes it difficult to trade-off the accuracy and the level of privacy protection when dealing with imbalanced data from multiple sources.To address this problem,a federated learning framework with adaptive differential privacy noise addition is pro-posed,which adopts a contribution proof algorithm based on the Shapley value to compute the contribution degree of cli-ents with different data sources,and based on the contribution degree,differentiated differential privacy noise is added for different clients in the process of gradient updating,and then personalized privacy protection is achieved.Theoretical and experimental analyses show that this framework can not only provide a more fine-grained level of privacy protection for different participants when facing multi-source unbalanced data,but also outperforms the traditional FL-DP algorithm by 1.3 percentage points in terms of model performance.关键词
联邦学习/差分隐私/沙普利值/不平衡数据Key words
federated learning/differential privacy/Shapley value/unbalanced data分类
信息技术与安全科学引用本文复制引用
裴浪涛,陈学斌,任志强,翟冉..面向多源数据的个性化联邦学习框架[J].计算机工程与应用,2024,60(19):278-287,10.基金项目
国家自然科学基金(U20A20179). （U20A20179）
