西安电子科技大学学报（自然科学版）2024，Vol.51Issue(3)：158-169,12.DOI:10.19665/j.issn1001-2400.20230706
可实现双向自适应差分隐私的联邦学习方案
Bidirectional adaptive differential privacy federated learning scheme
摘要
Abstract
With the explosive growth of personal data,the federated learning based on differential privacy can be used to solve the problem of data islands and preserve user data privacy.Participants share the parameters with noise to the central server for aggregation by training local data,and realize distributed machine learning training.However,there are two defects in this model:on the one hand,the data information in the process of parameters broadcasting by the central server is still compromised,with the risk of user privacy leakage;on the other hand,adding too much noise to parameters will reduce the quality of parameter aggregation and affect the model accuracy of federated learning.In order to solve the above problems,a bidirectional adaptive differential privacy federated learning scheme(Federated Learning Approach with Bidirectional Adaptive Differential Privacy,FedBADP)is proposed,which can adaptively add noise to the gradients transmitted by participants and central servers,and keep data security without affecting the model accuracy.Meanwhile,considering the performance limitations of the participants hardware devices,this model samples their gradients to reduce the communication overhead,and uses the RMSprop to accelerate the convergence of the model on the participants and central server to improve the accuracy of the model.Experiments show that our novel model can enhance the user privacy preserving while maintaining a good accuracy.关键词
双向自适应噪声/均方根传递/采样/差分隐私/联邦学习Key words
bidirectional adaptive noise/RMSprop/sampling/differential privacy/federated learning分类
计算机与自动化引用本文复制引用
李洋,徐进,朱建明,王友卫..可实现双向自适应差分隐私的联邦学习方案[J].西安电子科技大学学报（自然科学版）,2024,51(3):158-169,12.基金项目
国家重点研发计划(2017YFB1400700) （2017YFB1400700）
教育部人文社科项目(19YJCZH178) （19YJCZH178）
中央财经大学教育教学改革基金2022年度课题(2022ZXJG35) （2022ZXJG35）
中央财经大学新兴交叉学科建设项目资助 （）
