计算机工程2024，Vol.50Issue(3)：148-155,8.DOI:10.19678/j.issn.1000-3428.0067877
云-边融合的可验证隐私保护跨域联邦学习方案
Cloud-Edge Fusion Verifiable Privacy-Preserving Cross-Domain Federated Learning Scheme
摘要
Abstract
The rapid development of Federated Learning(FL)technology promotes collaborative training of gradient models using data from different end users.Its notable feature is that the training dataset does not leave the local device,and only gradient model updates are locally computed and shared,enabling edge servers to generate global gradient models.However,the heterogeneity between local devices can affect training performance,and shared gradient model updates pose privacy breaches and malicious tampering threats.This study proposes a verifiable privacy-preserving cross-domain FL scheme based on cloud-edge fusion.In the scheme,end users use single mask blinding technology to protect data privacy,vector inner product based signature algorithms to generate signatures for gradient models,and edge servers aggregate private data through blinding technology to generate deblinded aggregated signatures.This ensures the global gradient model is updated and the sharing process is tamper proof.It adopts multi-region weight forwarding technology to address the problem of limited computing resources and communication costs of devices in heterogeneous networks.The experimental results demonstrate that the proposed scheme can be safely and efficiently deployed in heterogeneous networks,and system experiments and simulations are performed on four benchmark datasets:MNIST,SVHN,CIFAR-10,and CIFAR-100.Compared with the classical federated learning scheme,the gradient model convergence speed of our scheme is improved by an average of 21.6%with comparable accuracy.关键词
联邦学习/全局梯度模型/数据隐私/可验证隐私保护/跨域训练Key words
Federated Learning(FL)/global gradient model/data privacy/verifiable privacy-preserving/cross-domain training分类
信息技术与安全科学引用本文复制引用
张晓均,李兴鹏,唐伟,郝云溥,薛婧婷..云-边融合的可验证隐私保护跨域联邦学习方案[J].计算机工程,2024,50(3):148-155,8.基金项目
国家自然科学基金(61902327) （61902327）
中国博士后科学基金(2020M681316) （2020M681316）
四川省自然科学青年基金(2023NSFSC1398) （2023NSFSC1398）
西南石油大学研究生教研教改项目(JY20ZD06). （JY20ZD06）
