FederatedNILM: A Distributed and Privacy-Preserving Framework for Non-Intrusive Load Monitoring Based on Federated Deep Learning-Reference-Cited by-同舟云学术

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FederatedNILM: A Distributed and Privacy-Preserving Framework for Non-Intrusive Load Monitoring Based on Federated Deep Learning

Published:2023-06-18 Issue: Volume: Page:
ISSN:
Container-title:2023 International Joint Conference on Neural Networks (IJCNN)
language:
Short-container-title:

Author:

Dai Shuang¹,Meng Fanlin²,Wang Qian³,Chen Xizhong⁴

Affiliation:

1. University of Exeter,Department of Engineering,Exeter,UK

2. Alliance Manchester Business School, University of Manchester,Manchester,UK

3. Durham University,Department of Computer Science,Durham,UK

4. School of Chemistry and Chemical Engineering, Shanghai Jiao Tong University,Shanghai,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10190990/10190992/10191549.pdf?arnumber=10191549

Reference26 articles.

1. A systematic review of technologies and solutions to improve security and privacy protection of citizens in the smart city

2. Smart Grids: Security and Privacy Issues

3. Privacy for Smart Meters: Towards Undetectable Appliance Load Signatures

4. Credential-Based Privacy-Preserving Power Request Scheme for Smart Grid Network

5. Edge Computing for IoT-Enabled Smart Grid: The Future of Energy

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. DP2-NILM: A distributed and privacy-preserving framework for non-intrusive load monitoring;Renewable and Sustainable Energy Reviews;2024-03

2. Advancing Power System Services With Privacy-Preserving Federated Learning Techniques: A Review;IEEE Access;2024

3. FPCNN: A fast privacy-preserving outsourced convolutional neural network with low-bandwidth;Knowledge-Based Systems;2024-01

4. Semi-Federated Learning Based on Autoencoder for Non-Intrusive Load Monitoring;2023 IEEE 23rd International Conference on Communication Technology (ICCT);2023-10-20

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