融合电力数据的纵向联邦学习企业排污预测模型  

Vertical federated learning enterprise emission prediction model with integration of electricity data

作　　者：王心妍 杜嘉程 钟李红 徐旺旺 刘伯宇 佘维[1,3] WANG Xinyan;DU Jiacheng;ZHONG Lihong;XU Wangwang;LIU Boyu;SHE Wei(School of Cyberspace Security,Zhengzhou University,Zhengzhou Henan 450002,China;Information and Telecommunication Company,State Grid Henan,Zhengzhou Henan 450000,China;Songshan Laboratory,Zhengzhou Henan 450046,China;School of Computer Science and Artificial Intelligence,Zhengzhou University,Zhengzhou Henan 450002,China)

机构地区：[1]郑州大学网络空间安全学院,郑州450002 [2]国网河南省电力公司信息通信分公司,郑州450000 [3]嵩山实验室,郑州450046 [4]郑州大学计算机与人工智能学院,郑州450002

出　　处：《计算机应用》2025年第2期518-525,共8页journal of Computer Applications

基　　金：河南省重点研发与推广专项(212102310039);嵩山实验室预研项目(YYYY022022003)。

摘　　要：针对企业排污难以监测和控制的问题,在考虑数据安全共享和隐私保护的前提下,提出一种融合电力数据的纵向联邦学习企业排污预测(VFL-EEP)模型。首先,在纵向联邦学习(VFL)框架下改进逻辑回归模型,从而在不泄露电力和环保企业排污监测数据的前提下,允许将数据的使用和模型的训练相分离;随后,改进逻辑回归算法使该算法能结合Paillier加密技术以保证模型的参数传递安全,从而有效解决VFL中参与方之间通信不安全的问题;最后,在仿真数据上实验,所提模型的排污预测结果与集中式逻辑回归模型的排污预测结果比较表明:所提模型在隐私安全的前提下融合电力数据,准确率、召回率、精确率和F1值分别提升了8.92%、7.62%、3.95%和11.86%,有效实现了隐私保护和模型性能的均衡。To address the problem of the difficulty of monitoring and controlling enterprise emissions,a Vertical Federated Learning Enterprise Emission Prediction(VFL-EEP) model with integration of electricity data was proposed by considering the premise of secure data sharing and privacy protection.Firstly,within the framework of Vertical Federated Learning(VFL),the logistic regression model was enhanced to allow the separation of data usage and model training without leaking the monitoring data of electricity and environmental protection enterprises.Then,the logistic regression algorithm was improved to incorporate with Paillier encryption technology for ensuring the security of model parameter transmission,thereby solving the issue of insecure communication among participants in VFL effectively.Finally,through experiments on simulated data,the pollution prediction results of the proposed model were compared with those of the centralized logistic regression model.The results show that the proposed model integrates electricity data under the premise of privacy security,and has the accuracy,recall,precision,and F1 value improved by 8.92%,7.62%,3.95%,and 11.86%,respectively,realizing the balance between privacy protection and model performance effectively.

关 键 词：纵向联邦学习 逻辑回归算法 隐私集合求交 Paillier同态加密 数据共享 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] E91[自动化与计算机技术—控制科学与工程]