首页|Personalized Federated Learning for Heterogeneous Residential Load Forecasting

Personalized Federated Learning for Heterogeneous Residential Load Forecasting

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Accurate load forecasting is critical for electricity production,transmission,and maintenance.Deep learning(DL)model has replaced other classical models as the most popular prediction models.However,the deep prediction model requires users to provide a large amount of private electricity consumption data,which has potential privacy risks.Edge nodes can federally train a global model through aggregation using federated learning(FL).As a novel distributed machine learning(ML)technique,it only exchanges model parameters without sharing raw data.However,existing forecasting methods based on FL still face challenges from data heterogeneity and privacy disclosure.Accordingly,we propose a user-level load forecasting system based on personalized federated learning(PFL)to address these issues.The obtained personalized model outperforms the global model on local data.Further,we introduce a novel differential privacy(DP)algorithm in the proposed system to provide an additional privacy guarantee.Based on the principle of generative adversarial network(GAN),the algorithm achieves the balance between privacy and prediction accuracy throughout the game.We perform simulation experiments on the real-world dataset and the experimental results show that the proposed system can comply with the requirement for accuracy and privacy in real load forecasting scenarios.

load forecastingpersonalized federated learningdifferential privacy

Xiaodong Qu、Chengcheng Guan、Gang Xie、Zhiyi Tian、Keshav Sood、Chaoli Sun、Lei Cui

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Shanxi Key Laboratory of Advanced Control and Equipment Intelligence,Taiyuan University of Science and Technology,Taiyuan 030024,China

Faculty of Engineering and Information Technology,University of Technology Sydney,Ultimo 2007,Australia

Centre for Cyber Security Research and Innovation,Deakin University,Melbourne 3125,Australia

School of Computer Science and Technology,Taiyuan University of Science and Technology,Taiyuan 030024,China

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Scientific and Technologial Innovation Programs of Higher Education Institutions in Shanxi,ChinaShanxi Key Research and Development ProgramShanxi Key Research and Development Program

2020L0338202102020101002202102020101005

2023

10.26599/BDMA.2022.9020043

大数据挖掘与分析(英文版)

大数据挖掘与分析(英文版)

CSCDEI
ISSN：
年,卷(期)：2023.6(4)
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