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

doi:10.11772/j.issn.1001-9081.2024020173

Journal of Computer Applications ›› 2025, Vol. 45 ›› Issue (2): 518-525.DOI: 10.11772/j.issn.1001-9081.2024020173

• Cyber security • Previous Articles

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

Xinyan WANG¹^,², Jiacheng DU², Lihong ZHONG¹^,³^,⁴, Wangwang XU¹, Boyu LIU², Wei SHE¹^,³()

^1.School of Cyberspace Security，Zhengzhou University，Zhengzhou Henan 450002，China
^2.Information and Telecommunication Company，State Grid Henan，Zhengzhou Henan 450000，China
^3.Songshan Laboratory，Zhengzhou Henan 450046，China
^4.School of Computer Science and Artificial Intelligence，Zhengzhou University，Zhengzhou Henan 450002，China

Received:2024-02-22 Revised:2024-04-02 Accepted:2024-04-17 Online:2024-06-04 Published:2025-02-10
Contact: Wei SHE
About author:WANG Xinyan， born in 1986， M. S.， senior engineer. Her interests include power data analysis， data security， blockchain， artificial intelligence.
DU Jiacheng， born in 1991， M. S.， engineer. His research interests include big data analysis and application， blockchain.
ZHONG Lihong， born in 1997， Ph. D. candidate. Her research interests include machine learning， recommendation systems， federated learning.
XU Wangwang， born in 1999， M. S. candidate. His research interests include machine learning， federated learning.
LIU Boyu， born in 1979， M. S.， senior engineer. His research interests include big data analysis and application， data security， data management.
Supported by:
Key Research and Development and Promotion Project in Henan Province(212102310039);Songshan Laboratory Pre-research Project(YYYY022022003)

融合电力数据的纵向联邦学习企业排污预测模型

王心妍¹^,², 杜嘉程², 钟李红¹^,³^,⁴, 徐旺旺¹, 刘伯宇², 佘维¹^,³()

^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

通讯作者: 佘维
作者简介:王心妍（1986—），女，河南安阳人，高级工程师，硕士，主要研究方向：电力数据分析、数据安全、区块链、人工智能
杜嘉程（1991—），男，河南郑州人，工程师，硕士，主要研究方向：大数据分析与应用、区块链
钟李红（1997—），女，四川射洪人，博士研究生，主要研究方向：机器学习、推荐系统、联邦学习
徐旺旺（1999—），男，河南濮阳人，硕士研究生，主要研究方向：机器学习、联邦学习
刘伯宇（1979—），男，河南郑州人，高级工程师，硕士，主要研究方向：大数据分析与应用、数据安全、数据管理；
基金资助:
河南省重点研发与推广专项(212102310039);嵩山实验室预研项目(YYYY022022003)

Abstract

Abstract:

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.

Key words: Vertical Federated Learning (VFL), logistic regression model, Private Set Intersection （PSI）, Paillier homomorphic encryption, data sharing

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。

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

CLC Number:

TP183

Xinyan WANG, Jiacheng DU, Lihong ZHONG, Wangwang XU, Boyu LIU, Wei SHE. Vertical federated learning enterprise emission prediction model with integration of electricity data[J]. Journal of Computer Applications, 2025, 45(2): 518-525.

王心妍, 杜嘉程, 钟李红, 徐旺旺, 刘伯宇, 佘维. 融合电力数据的纵向联邦学习企业排污预测模型[J]. 《计算机应用》唯一官方网站, 2025, 45(2): 518-525.

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

融合电力数据的纵向联邦学习企业排污预测模型

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