火力与指挥控制2025，Vol.50Issue(1)：189-194,6.DOI:10.3969/j.issn.1002-0640.2025.01.026
基于深度学习的联邦学习中数据隐私保护方法
A Deep Learning-based Data Privacy Protection Method for Federated Learning
摘要
Abstract
Split learning(SL)enables privacy protection field become a research hotspot by allowing clients to collaboratively train a deep learning model with the server without sharing raw data.However,split learning still faces data reconstruction attacks that threaten participants'sensitive information.Therefore,binary split learning-based data privacy protection(BSLP)algorithm is proposed.In BLDP algorithm,the binarization of the local model trained by the client is conducted and the data leakage loss caused by the output value of split layer is reduced.At the same time,the BSLP algorithm quotes the differential privacy mechanism to add noise to the data and then generalize the data.Four typical datasets are experimented and the classification accuracy rate and privacy protection performance of BSLP algorithm are analyzed.The analysis results show that the classification accuracy rate of the proposed BSLP algorithm on the MNIST dataset is 97%,and the KL divergence is 3.68,which verifies the fact that the BSLP algorithm has stronger privacy protection performance.关键词
联邦学习/隐私保护/拆分学习/二值化/差分隐私Key words
federated learning/privacy protection/split learning/binarization/differential privacy分类
信息技术与安全科学引用本文复制引用
田根源..基于深度学习的联邦学习中数据隐私保护方法[J].火力与指挥控制,2025,50(1):189-194,6.基金项目
河南省科技攻关计划资助项目(212102210515) （212102210515）
