{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T21:06:25Z","timestamp":1786136785202,"version":"3.56.0"},"reference-count":46,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"5","license":[{"start":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T00:00:00Z","timestamp":1725148800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T00:00:00Z","timestamp":1725148800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T00:00:00Z","timestamp":1725148800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"Overseas Visiting Doctoral Fellowship"},{"DOI":"10.13039\/501100001843","name":"Science and Engineering Research Board","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001843","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Sustain. Comput."],"published-print":{"date-parts":[[2024,9]]},"DOI":"10.1109\/tsusc.2024.3370837","type":"journal-article","created":{"date-parts":[[2024,2,28]],"date-time":"2024-02-28T18:59:35Z","timestamp":1709146775000},"page":"766-777","source":"Crossref","is-referenced-by-count":14,"title":["A Robust and Privacy-Aware Federated Learning Framework for Non-Intrusive Load Monitoring"],"prefix":"10.1109","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6164-2240","authenticated-orcid":false,"given":"Vidushi","family":"Agarwal","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Indian Institute of Technology Ropar, Bara Phool, Punjab, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6711-5502","authenticated-orcid":false,"given":"Omid","family":"Ardakanian","sequence":"additional","affiliation":[{"name":"Department of Computing Science, University of Alberta, Edmonton, AB, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6652-9669","authenticated-orcid":false,"given":"Sujata","family":"Pal","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Indian Institute of Technology Ropar, Bara Phool, Punjab, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-78262-1_243"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/5.192069"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1007\/s12053-008-9009-7"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TCE.2011.5735484"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1145\/3427771.3429390"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2929141"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.03762"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.3390\/s22082926"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1002\/int.22876"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1049\/enc2.12055"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TGCN.2022.3167392"},{"key":"ref12","article-title":"A federated learning framework for non-intrusive load monitoring","author":"Wang","year":"2021"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1145\/3447555.3464873"},{"key":"ref14","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"Proc. Artif. Intell. Statist.","author":"McMahan"},{"key":"ref15","first-page":"2938","article-title":"How to backdoor federated learning","volume-title":"Proc. Int. Conf. Artif. Intell. Statist.","author":"Bagdasaryan"},{"key":"ref16","first-page":"634","article-title":"Analyzing federated learning through an adversarial lens","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Bhagoji"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM51629.2021.00129"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2020.10.007"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2021.07.098"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE53745.2022.00077"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v26i1.8162"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11873"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33011150"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.3301850"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/EPEC47565.2019.9074816"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.3390\/s19235236"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TSUSC.2022.3175941"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2016.2631238"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1016\/j.scs.2021.102731"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1145\/2821650.2821672"},{"key":"ref31","article-title":"BERT: Pre-training of deep bidirectional transformers for language understanding","author":"Devlin","year":"2018"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1145\/3439729"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2021.3062722"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1145\/3551636"},{"key":"ref35","first-page":"1605","article-title":"Local model poisoning attacks to byzantine-robust federated learning","volume-title":"Proc. 29th USENIX Secur. Symp.","author":"Fang"},{"key":"ref36","article-title":"Mitigating sybils in federated learning poisoning","author":"Fung","year":"2018"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/CDC51059.2022.9993097"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN54540.2023.10191549"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/DASC\/PiCom\/CBDCom\/Cy55231.2022.9927999"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.7717\/peerj-cs.1049"},{"key":"ref41","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume-title":"Proc. Mach. Learn. Syst.","volume":"2","author":"Li","year":"2020"},{"key":"ref42","first-page":"1126","article-title":"Model-agnostic meta-learning for fast adaptation of deep networks","volume-title":"Proc. 34th Int. Conf. Mach. Learn.","author":"Finn"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2018.2806994"},{"key":"ref44","first-page":"59","article-title":"REDD: A public data set for energy disaggregation research","volume-title":"Proc. Workshop Data Mining Appl. Sustainability","author":"Kolter"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2015.7"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1002\/widm.1265"}],"container-title":["IEEE Transactions on Sustainable Computing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/7274860\/10712654\/10452825.pdf?arnumber=10452825","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,10]],"date-time":"2024-10-10T17:29:47Z","timestamp":1728581387000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10452825\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9]]},"references-count":46,"journal-issue":{"issue":"5"},"URL":"https:\/\/doi.org\/10.1109\/tsusc.2024.3370837","relation":{},"ISSN":["2377-3782","2377-3790"],"issn-type":[{"value":"2377-3782","type":"electronic"},{"value":"2377-3790","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9]]}}}