物联网学报2024，Vol.8Issue(4)：54-69,16.DOI:10.11959/j.issn.2096-3750.2024.00440
面向联邦算力物联网的隐私预算自适应优化方案
A privacy budget adaptive optimization scheme for federated computing power Internet of things
摘要
Abstract
Federated computing power Internet of things(IoT)is designed to deeply integrate computing power with IoT resources,facilitating the efficient utilization of vast and ubiquitously dispersed IoT data and heterogeneous resources through federated learning.Faced with the threats of emerging privacy attacks,e.g.,model inversion attacks and gradient leakage attacks,the academic and industrial communities have widely investigated and applied differential privacy(DP)as an effective privacy protection technique.However,two severe challenges have not been taken into account in the exist-ing DP budget settings,i.e.,data heterogeneity issue of local computing power nodes and the fairness of privacy budget al-location,which lead to a significant loss in model accuracy.Therefore,an adaptive optimization scheme for privacy bud-get was proposed in federated computing power IoT,which was called federated learning based on Cramér-Rao lower bound differential privacy(FedCDP).In specific,to adaptively adjust privacy budgets,the privacy budget estimates for edge computing power nodes based on the Cramér-Rao lower bound theory were analyzed.Furthermore,by assessing the simi-larity between the local model and the aggregated model,as well as their respective privacy budget proportions,the global contribution of each node was determined,which was used to fairly,also in real time,optimize and adjust the privacy bud-get settings in conjunction with the estimated privacy budget.Through rigorous theoretical analysis,FedCDP achieves ε-DP for local models,and ensures the convergence of the global model.Experimental results on multiple public datasets show that the proposed scheme improves the accuracy of the global model by up to 10.19%under the premise of satisfy-ing the same privacy protection requirements.关键词
联邦算力物联网/差分隐私/隐私预算/自适应优化Key words
federated computing power IoT/differential privacy/privacy budget/adaptive optimization分类
信息技术与安全科学引用本文复制引用
马文玉,陈谦,胡宇翔,闫皓楠,胡涛,伊鹏..面向联邦算力物联网的隐私预算自适应优化方案[J].物联网学报,2024,8(4):54-69,16.基金项目
国家重点研发计划(No.2022YFB2901500) （No.2022YFB2901500）
国家自然科学基金资助项目(No.62402373)The National Key Research and Development Program of China(No.2022YFB2901500),The National Natural Science Foundation of China(No.62402373) （No.62402373）
