{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T05:07:06Z","timestamp":1770354426517,"version":"3.49.0"},"reference-count":26,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"4","license":[{"start":{"date-parts":[[2022,10,1]],"date-time":"2022-10-01T00:00:00Z","timestamp":1664582400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2022,10,1]],"date-time":"2022-10-01T00:00:00Z","timestamp":1664582400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,10,1]],"date-time":"2022-10-01T00:00:00Z","timestamp":1664582400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100010418","name":"Institute for Information and Communications Technology Promotion","doi-asserted-by":"publisher","award":["2021-0-00739"],"award-info":[{"award-number":["2021-0-00739"]}],"id":[{"id":"10.13039\/501100010418","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001321","name":"National Research Foundation","doi-asserted-by":"publisher","award":["2021R1A4A3022102"],"award-info":[{"award-number":["2021R1A4A3022102"]}],"id":[{"id":"10.13039\/501100001321","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Emerg. Topics Comput."],"published-print":{"date-parts":[[2022,10,1]]},"DOI":"10.1109\/tetc.2022.3142886","type":"journal-article","created":{"date-parts":[[2022,1,20]],"date-time":"2022-01-20T20:25:51Z","timestamp":1642710351000},"page":"2092-2098","source":"Crossref","is-referenced-by-count":30,"title":["Fuzzy Clustered Federated Learning Algorithm for Solar Power Generation Forecasting"],"prefix":"10.1109","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1767-1631","authenticated-orcid":false,"given":"Eungeun","family":"Yoo","sequence":"first","affiliation":[{"name":"Department of Computer and Information Science, Korea University, Sejong, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9067-445X","authenticated-orcid":false,"given":"Haneul","family":"Ko","sequence":"additional","affiliation":[{"name":"Department of Computer and Information Science, Korea University, Sejong, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1085-1568","authenticated-orcid":false,"given":"Sangheon","family":"Pack","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Korea University, Seoul, Korea"}]}],"member":"263","reference":[{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2020.2987916"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2020.2986024"},{"key":"ref12","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"mcmahan","year":"2017","journal-title":"Proc 20th Int Conf Artif Intell Statist"},{"key":"ref13","first-page":"3557","article-title":"Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach","author":"fallah","year":"2020","journal-title":"Proc Annu 34th Conf Neural Inf Process Syst"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2020.3035807"},{"key":"ref15","author":"wang","year":"2019"},{"key":"ref16","author":"sattler","year":"2019"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN48605.2020.9207469"},{"key":"ref18","author":"ghosh","year":"2021"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/BigComp51126.2021.00039"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TSTE.2017.2694340"},{"key":"ref3","year":"0"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.solener.2010.08.011"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2017.2728480"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3024901"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2923006"},{"key":"ref2","year":"0"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/SMC.2016.7844673"},{"key":"ref1","year":"0"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3094089"},{"key":"ref22","year":"1999","journal-title":"Frank H&#x00F6;ppner Frank Klawonn Rudolf Kruse Thomas Runkler Fuzzy Cluster Analysis Methods for Classifications Data Analysis and Image Recognition"},{"key":"ref21","year":"0"},{"key":"ref24","year":"2014"},{"key":"ref23","doi-asserted-by":"crossref","first-page":"1275","DOI":"10.1016\/j.patrec.2004.11.022","article-title":"A cluster validity index for fuzzy clustering","volume":"26","author":"lungwua","year":"2005","journal-title":"Pattern Recognit Lett"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TETC.2022.3142886"},{"key":"ref25","first-page":"281","article-title":"Some methods for classification and analysis of multivariate observations","author":"macqueen","year":"1967","journal-title":"Proc Berkeley Symp Math Statist Probability"}],"container-title":["IEEE Transactions on Emerging Topics in Computing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6245516\/9970409\/09687124.pdf?arnumber=9687124","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,26]],"date-time":"2022-12-26T19:14:01Z","timestamp":1672082041000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9687124\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,1]]},"references-count":26,"journal-issue":{"issue":"4"},"URL":"https:\/\/doi.org\/10.1109\/tetc.2022.3142886","relation":{},"ISSN":["2168-6750","2376-4562"],"issn-type":[{"value":"2168-6750","type":"electronic"},{"value":"2376-4562","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,1]]}}}