电力信息与通信技术2024，Vol.22Issue(11)：25-33,9.DOI:10.16543/j.2095-641x.electric.power.ict.2024.11.04
基于匿名性差分隐私联邦学习的负荷预测模型训练方法
A Training Method for Load Forecasting Models Based on Anonymity Differential Privacy Federated Learning
摘要
Abstract
With the continuous reform of the electricity market,load forecasting has become a crucial aspect.Edge devices in the power system tend to keep the data locally to ensure the privacy protection of electricity consumption data,which will hinder the multi-device cooperation in training load forecasting models to some extent,while the federated learning technique can complete the load forecasting model training under the condition that the data is kept locally.However,current research suggests that a centralized server for training can speculate client datasets leading to privacy breaches.For this reason,this paper proposes a client-third-party server-server federated learning framework with anonymity privacy protection,which achieves anonymity in transmitting model parameters while ensuring communication efficiency,and an improved localized differential privacy federated learning(LDP-FL)algorithm to ensure the privacy security of the clients,while utilizing the cosine function to filter the gradients involved in aggregation to exclude malicious clients.The experimental results show that the proposed method has high accuracy and convergence rate,and has a significant role in promoting the deepening of power load forecasting technology.关键词
负荷预测/联邦学习/差分隐私/自适应数据扰动/余弦相似度Key words
load forecasting/federated learning/differential privacy/adaptive data perturbation/cosine similarity分类
信息技术与安全科学引用本文复制引用
罗凯鸿,徐茹枝,夏迪娅,杨鑫..基于匿名性差分隐私联邦学习的负荷预测模型训练方法[J].电力信息与通信技术,2024,22(11):25-33,9.基金项目
国家自然科学基金项目(62372173). （62372173）
