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Understanding power usage patterns through NILM plays an important role in reducing energy costs and achieving carbon reduction goals. However, privacy concerns often deter consumers from sharing their electricity consumption data. To address these privacy concerns, Federated Learning (FL) has been introduced in NILM, which enables the training of NILM models while keeping power consumers\u2019 data locally. However, FL\u2019s reliance on a single global model leads to poor performance on clients with unique power consumption patterns. In response to this challenge, we present a Personalized Federated Learning NILM algorithm (PerFedNILM), a practical personalized FL approach for NILM. PerFedNILM limits the local update bias across clients and trains personalized models for individual clients to improve load-monitoring performance. In addition, it mitigates the negative impact of client dropout, which is a common issue in practice. Our experiments on using real-world energy data demonstrate that PerFedNILM outperforms previous FL-based NILM methods, especially in client dropout scenarios.<\/jats:p>","DOI":"10.1007\/s44244-024-00016-8","type":"journal-article","created":{"date-parts":[[2024,4,15]],"date-time":"2024-04-15T19:02:00Z","timestamp":1713207720000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Perfednilm: a practical personalized federated learning-based non-intrusive load monitoring"],"prefix":"10.1007","volume":"2","author":[{"given":"Zibin","family":"Pan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haosheng","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chi","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haijin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5446-2655","authenticated-orcid":false,"given":"Junhua","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,4,16]]},"reference":[{"key":"16_CR1","unstructured":"Arivazhagan MG, Aggarwal V, Singh AK, Choudhary S (2019) Federated learning with personalization layers. arXiv preprint arXiv:1912.00818"},{"key":"16_CR2","doi-asserted-by":"crossref","unstructured":"Azad MI, Rajabi R, Estebsari A (2023) Non-intrusive load monitoring (nilm) using deep neural networks: a review. arXiv preprint arXiv:2306.05017","DOI":"10.1109\/EEEIC\/ICPSEurope57605.2023.10194770"},{"key":"16_CR3","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1186\/s42162-018-0038-y","volume":"1","author":"K Bao","year":"2018","unstructured":"Bao K, Ibrahimov K, Wagner M, Schmeck H (2018) Enhancing neural non-intrusive load monitoring with generative adversarial networks. 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