A Federated Learning Model With Short Sequence To Point Mechanism For Smart Home Energy Disaggregation
Author:
Affiliation:
1. Lakehead University,Department of Computer Science,Thunder Bay,Canada
2. Lakehead University,Department of Software Engineering,Thunder Bay,Canada
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9912737/9912463/09912852.pdf?arnumber=9912852
Cited by 5 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. Exploring Deep Federated Learning for the Internet of Things: A GDPR-Compliant Architecture;IEEE Access;2024
2. Feeder Power Disaggregation: A Data-Efficient Matrix Completion Approach;2023 IEEE Power & Energy Society General Meeting (PESGM);2023-07-16
3. Federated Non-Intrusive Load Monitoring for Smart Homes Utilizing Attention-Based Aggregation;2023 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT);2023-07-13
4. Variational Auto-Encoder Model and Federated Approach for Non-Intrusive Load Monitoring in Smart Homes;2023 IEEE Symposium on Computers and Communications (ISCC);2023-07-09
5. Neural Load Disaggregation: Meta-Analysis, Federated Learning and Beyond;Energies;2023-01-16
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