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- Title
Federated Learning for Non-intrusive Load Monitoring.
- Authors
Zhaorui Meng; Xiaozhu Xie; Yanqi Xie
- Abstract
In the realm of non-intrusive load monitoring (NILM), extant deep learning approaches suffer from limitations including inadequate data samples, inadequate model generalization capacity, and insufficient safeguards for data privacy. To overcome these issues, this paper puts forward a novel NILM approach that leverages DeepAR to build a load monitoring model and incorporates federated learning and local fine-tuning methods to develop a non-intrusive load monitoring framework. Utilizing decentralized training, the proposed methodology facilitates iterative updates to model parameters through server-side aggregation, thereby enabling the collaborative construction of a monitoring model whilst maintaining strict confidentiality of individual customer data. The results of experiments conducted on the REDD dataset demonstrate that the approach outlined in this paper can markedly enhance the accuracy of load identification for frequently utilized electrical appliances.
- Subjects
DEEP learning; DATA privacy
- Publication
IAENG International Journal of Applied Mathematics, 2023, Vol 53, Issue 3, p945
- ISSN
1992-9978
- Publication type
Article