{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T17:56:58Z","timestamp":1775066218091,"version":"3.50.1"},"reference-count":26,"publisher":"IEEE","license":[{"start":{"date-parts":[[2023,6,18]],"date-time":"2023-06-18T00:00:00Z","timestamp":1687046400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,6,18]],"date-time":"2023-06-18T00:00:00Z","timestamp":1687046400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,6,18]]},"DOI":"10.1109\/ijcnn54540.2023.10191549","type":"proceedings-article","created":{"date-parts":[[2023,8,2]],"date-time":"2023-08-02T17:30:03Z","timestamp":1690997403000},"page":"01-08","source":"Crossref","is-referenced-by-count":15,"title":["FederatedNILM: A Distributed and Privacy-Preserving Framework for Non-Intrusive Load Monitoring Based on Federated Deep Learning"],"prefix":"10.1109","author":[{"given":"Shuang","family":"Dai","sequence":"first","affiliation":[{"name":"University of Exeter,Department of Engineering,Exeter,UK"}]},{"given":"Fanlin","family":"Meng","sequence":"additional","affiliation":[{"name":"Alliance Manchester Business School, University of Manchester,Manchester,UK"}]},{"given":"Qian","family":"Wang","sequence":"additional","affiliation":[{"name":"Durham University,Department of Computer Science,Durham,UK"}]},{"given":"Xizhong","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Chemistry and Chemical Engineering, Shanghai Jiao Tong University,Shanghai,China"}]}],"member":"263","reference":[{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.iot.2022.100584"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-45050-6"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/SMARTGRID.2010.5622047"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/GLOCOM.2011.6134566"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.3390\/en15176140"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2020.103785"},{"key":"ref2","article-title":"Non-intrusive load monitoring: A review and outlook","author":"klemenjak","year":"2016","journal-title":"ArXiv Preprint"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.seta.2022.102648"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1002\/cpe.4528"},{"key":"ref16","first-page":"1","article-title":"Information theoretic privacy for smart meters","author":"g\u00fcnd\u00fcz","year":"0","journal-title":"2013 Information Theory and Applications Workshop (ITA)"},{"key":"ref19","first-page":"756","article-title":"Dïot: A federated self-learning anomaly detection system for iot","author":"nguyen","year":"0","journal-title":"2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS)"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TC.2021.3130402"},{"key":"ref24","article-title":"Nilm as a regression versus classification problem: the importance of thresholding","author":"precioso","year":"2020","journal-title":"ArXiv Preprint"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2019.2938068"},{"key":"ref25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/sdata.2015.7","article-title":"The uk-dale dataset, domestic appliance-level electricity demand and whole-house demand from five uk homes","volume":"2","author":"kelly","year":"2015","journal-title":"Scientific Data"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1145\/3368926.3369705"},{"key":"ref22","article-title":"A survey on federated learning systems: vision, hype and reality for data privacy and protection","author":"li","year":"2019","journal-title":"ArXiv Preprint"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-15-9129-7_16"},{"key":"ref8","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"mcmahan","year":"2017","journal-title":"Artificial Intelligence and Statistics"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.3390\/s19235236"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.106775"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1145\/2821650.2821672"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.scs.2020.102411"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2022.3169398"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.3390\/en12142804"}],"event":{"name":"2023 International Joint Conference on Neural Networks (IJCNN)","location":"Gold Coast, Australia","start":{"date-parts":[[2023,6,18]]},"end":{"date-parts":[[2023,6,23]]}},"container-title":["2023 International Joint Conference on Neural Networks (IJCNN)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10190990\/10190992\/10191549.pdf?arnumber=10191549","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,21]],"date-time":"2023-08-21T17:42:34Z","timestamp":1692639754000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10191549\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,18]]},"references-count":26,"URL":"https:\/\/doi.org\/10.1109\/ijcnn54540.2023.10191549","relation":{},"subject":[],"published":{"date-parts":[[2023,6,18]]}}}