电力信息与通信技术2024，Vol.22Issue(7)：18-26,9.DOI:10.16543/j.2095-641x.electric.power.ict.2024.07.03
基于差分隐私的个性化联邦电力负荷预测方案
A Personalized Federal Power Load Forecasting Scheme Based on Differential Privacy
摘要
Abstract
In order to achieve a power load forecasting scheme with both model personalization and privacy-preserving personalization,this paper proposes a personalized federal power load forecasting scheme based on differential privacy. The scheme performs cluster-based training based on the missing cases and temporal features of data to obtain a local personalized model applicable to local data. On this basis,a personalized differential privacy protection scheme is proposed,which adjusts the allocation of the privacy budget according to the distance from the client to the current cluster center to ensure the data security and achieve the personalization of privacy protection at the client level. Experiments show that the algorithm can be trained to obtain a personalization model with better utility while ensuring data security.关键词
电力负荷预测/个性化联邦学习/差分隐私/隐私保护/隐私预算/聚类Key words
power load forecasting/personalized federal learning/differential privacy/privacy protection/privacy budget/clustering分类
信息技术与安全科学引用本文复制引用
谭智文,徐茹枝,关志涛..基于差分隐私的个性化联邦电力负荷预测方案[J].电力信息与通信技术,2024,22(7):18-26,9.基金项目
国家电网有限公司总部科技项目资助"面向新型配电系统的网络安全动态防御关键技术深化研究"(5400-202340217A-1-1-ZN). （5400-202340217A-1-1-ZN）
