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personalized federated learning for Heterogeneous Residential Load Forecasting

Big Data Mining and Analytics 2023年第4期6卷 421-432页

作者： Xiaodong Qu Chengcheng Guan Gang Xie Zhiyi Tian Keshav Sood Chaoli Sun Lei Cui the Shanxi Key Laboratory of Advanced Control and Equipment Intelligence Taiyuan University of Science and TechnologyTaiyuan 030024China the Faculty of Engineering and Information Technology University of Technology SydneyUltimo 2007Australia the Centre for Cyber Security Research and Innovation Deakin UniversityMelbourne 3125Australia the School of Computer Science and Technology Taiyuan University of Science and TechnologyTaiyuan 030024China

Accurate load forecasting is critical for electricity production,transmission,and *** learning(DL)model has replaced other classical models as the most popular prediction ***,the deep prediction model requires users to provide a large amount of private electricity consumption data,which has potential privacy *** 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 ***,existing forecasting methods based on FL still face challenges from data heterogeneity and privacy ***,we propose a user-level load forecasting system based on personalized federated learning(PFL)to address these *** obtained personalized model outperforms the global model on local ***,we introduce a novel differential privacy(DP)algorithm in the proposed system to provide an additional privacy *** on the principle of generative adversarial network(GAN),the algorithm achieves the balance between privacy and prediction accuracy throughout the *** 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.

关键词： load forecasting personalized federated learning differential privacy

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FedFV: A personalized federated learning Framework for Finger Vein Authentication

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Machine Intelligence Research 2023年第5期20卷 683-696页

作者： Feng-Zhao Lian Jun-Duan Huang Ji-Xin Liu Guang Chen Jun-Hong Zhao Wen-Xiong Kang School of Automation Science and Engineering South China University of TechnologyGuangzhou 510641China GRG Banking Equipment Co.Ltd. Guangzhou 510663China School of Automation Guangdong University of Petrochemical TechnologyMaoming 525000China

Most finger vein authentication systems suffer from the problem of small sample ***,the data augmentation can alleviate this problem to a certain extent but did not fundamentally solve the problem of category *** the researchers resort to pre-training or multi-source data joint training methods,but these methods will lead to the problem of user privacy *** view of the above issues,this paper proposes a federated learning-based finger vein authentication framework(FedFV)to solve the problem of small sample size and category diversity while protecting user *** training under FedFV,each client can share the knowledge learned from its user′s finger vein data with the federated client without causing template *** addition,we further propose an efficient personalized federated aggregation algorithm,named federated weighted proportion reduction(FedWPR),to tackle the problem of non-independent identically distribution caused by client diversity,thus achieving the best performance for each *** thoroughly evaluate the effectiveness of FedFV,comprehensive experiments are conducted on nine publicly available finger vein *** results show that FedFV can improve the performance of the finger vein authentication system without directly using other client *** the best of our knowledge,FedFV is the first personalized federated finger vein authentication framework,which has some reference value for subsequent biometric privacy protection research.

关键词： Finger vein personalized federated learning privacy protection biometric authentication.

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PFL-DSSE:A personalized federated learning Approach for Distribution System State Estimation

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CSEE Journal of Power and Energy Systems 2024年第5期10卷 2265-2270页

作者： Huayi Wu Zhao Xu Jiaqi Ruan Xianzhuo Sun Department of Electrical and Electronic Engineering The Hong Kong Polytechnic UniversityHong Kong SARChina Department of Electrical and Electronic Engineering Shenzhen Research InstituteResearch Institute of Smart EnergyThe Hong Kong Polytechnic UniversityHong Kong SARChina. Department of Electrical and Electronic Engineering The Hong Kong Polytechnic UniversityHong Kong SARChina.

A centralized framework-based data-driven framework for active distribution system state estimation(DSSE)has been widely ***,it is challenged by potential data privacy breaches due to the aggregation of raw measurement data in a data center.A personalized federated learningbased DSSE method(PFL-DSSE)is proposed in a decentralized training framework for *** validation confirms that PFL-DSSE can effectively and efficiently maintain data confidentiality and enhance estimation accuracy.

关键词： Terms-Distribution system state estimation personalized federated learning privacy protection

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MpFedcon: Model-Contrastive personalized federated learning with the Class Center

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Wuhan University Journal of Natural Sciences 2022年第6期27卷 508-520页

作者： LI Xingchen FANG Zhijun SHI Zhicai School of Electronic and Electrical Engineering Shanghai University of Engineering ScienceShanghai 201620.China

federated learning is an emerging distributed privacypreserving framework in which parties are trained collaboratively by sharing model or gradient updates instead of sharing private data. However, the heterogeneity of local data distribution poses a significant challenge. This paper focuses on the label distribution skew, where each party can only access a partial set of the whole class set. It makes global updates drift while aggregating these biased local models. In addition, many studies have shown that deep leakage from gradients endangers the reliability of federated learning. To address these challenges, this paper propose a new personalized federated learning method named MpFedcon. It addresses the data heterogeneity problem and privacy leakage problem from global and local perspectives. Our extensive experimental results demonstrate that MpFedcon yields effective resists on the label leakage problem and better performance on various image classification tasks, robust in partial participation settings, non-iid data,and heterogeneous parties.

关键词： personalized federated learning layered network model contrastive learning gradient leakage

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