{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T13:20:44Z","timestamp":1775913644090,"version":"3.50.1"},"reference-count":62,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2025]]},"DOI":"10.1109\/access.2025.3630992","type":"journal-article","created":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T18:49:34Z","timestamp":1762800574000},"page":"193356-193376","source":"Crossref","is-referenced-by-count":2,"title":["Adaptive Federated Learning for Robust PV Power Forecasting Under Weather-Induced Concept Drift"],"prefix":"10.1109","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7075-1609","authenticated-orcid":false,"given":"Hamed","family":"Moayyed","sequence":"first","affiliation":[{"name":"Department of Engineering, Wake Forest University, Winston-Salem, NC, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4896-9809","authenticated-orcid":false,"given":"Hussein","family":"Abdeltawab","sequence":"additional","affiliation":[{"name":"Department of Engineering, Wake Forest University, Winston-Salem, NC, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8597-3383","authenticated-orcid":false,"given":"Luis","family":"Gomes","sequence":"additional","affiliation":[{"name":"GECAD–Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, LASI–Intelligent Systems Associate Laboratory, ISEP, Polytechnic of Porto, Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4560-9544","authenticated-orcid":false,"given":"Zita","family":"Vale","sequence":"additional","affiliation":[{"name":"GECAD–Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, LASI–Intelligent Systems Associate Laboratory, ISEP, Polytechnic of Porto, Porto, Portugal"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2906402"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3270041"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2909561"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3024901"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2983159"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2946057"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3169785"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3095420"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/ENERGYCON.2016.7514029"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-7908-2604-3_16"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/347090.347107"},{"key":"ref12","first-page":"319","article-title":"Adaptive learning from evolving data streams","volume-title":"Proc. 8th Int. Symp. Intell. Data Anal. (IDA)","author":"Bifet"},{"key":"ref13","first-page":"2673","article-title":"Online passive-aggressive algorithms","volume":"7","author":"Crammer","year":"2006","journal-title":"J. Mach. Learn. Res."},{"key":"ref14","article-title":"Adam: A method for stochastic optimization","author":"Kingma","year":"2014","journal-title":"arXiv:1412.6980"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.solener.2009.05.016"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/ISGT.2015.7131880"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2025.3606910"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1145\/1102351.1102408"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/icsmc.2005.1571498"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/5326.983933"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-017-5642-8"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2011.2160459"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.32692\/IJDI-ERET\/11.2.2022.2211"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1016\/j.egyai.2025.100540"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-28645-5_29"},{"key":"ref26","first-page":"77","article-title":"Early drift detection method","volume-title":"Proc. 4th Int. Workshop Knowl. Discovery Data Streams","author":"Baena-Garc\u00eda"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611972771.42"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.2307\/2333009"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1002\/0470841613"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3025860"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1016\/j.egyr.2024.12.078"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3165798"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-016-0043-6"},{"key":"ref34","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"Proc. Int. Conf. Artif. Intell. Stat. (AISTATS)","author":"McMahan"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1002\/9781119834052.ch19"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1016\/j.enconman.2022.115852"},{"key":"ref37","article-title":"On the convergence of FedAvg on non-IID data","author":"Li","year":"2019","journal-title":"arXiv:1907.02189"},{"key":"ref38","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume-title":"Proc. Mach. Learn. Syst. (MLSys)","author":"Tian"},{"key":"ref39","article-title":"An empirical study of efficiency and privacy of federated learning algorithms","author":"Zahri","year":"2023","journal-title":"arXiv:2312.15375"},{"key":"ref40","article-title":"Federated learning based on dynamic regularization","author":"Alp Emre Acar","year":"2021","journal-title":"arXiv:2111.04263"},{"key":"ref41","article-title":"Adaptive personalized federated learning","author":"Deng","year":"2020","journal-title":"arXiv:2003.13461"},{"key":"ref42","article-title":"Overcoming forgetting in federated learning on non-IID data","author":"Shoham","year":"2019","journal-title":"arXiv:1910.07796"},{"key":"ref43","article-title":"Not all federated learning algorithms are created equal: A performance evaluation study","author":"Baumgart","year":"2024","journal-title":"arXiv:2403.17287"},{"key":"ref44","article-title":"FedDAA: Dynamic client clustering for concept drift adaptation in federated learning","author":"Peng","year":"2025","journal-title":"arXiv:2506.21054"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2023.110230"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1016\/j.dcan.2024.02.001"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1049\/rpg2.12243"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1016\/j.seta.2022.102326"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i4.20327"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/PEDG54999.2022.9923109"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2023.121919"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3103126"},{"key":"ref53","author":"Gomes","year":"2021","journal-title":"Photovoltaic Generation Data, for 3 Years, Regarding the 2022-3 Competition on Solar Generation Forecasting"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2023\/759"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1007\/s41884-018-0002-8"},{"key":"ref56","article-title":"LEAF: A benchmark for federated settings","author":"Caldas","year":"2018","journal-title":"arXiv:1812.01097"},{"key":"ref57","article-title":"Federated learning with non-IID data","author":"Zhao","year":"2018","journal-title":"arXiv:1806.00582"},{"key":"ref58","article-title":"A field guide to federated optimization","author":"Wang","year":"2021","journal-title":"arXiv:2107.06917"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2923006"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3148821"},{"key":"ref61","article-title":"Metrics for evaluating the accuracy of solar power forecasting","author":"Zhang","year":"2013"},{"key":"ref62","author":"Aaslid","year":"2021","journal-title":"Rye Microgrid Load and Generation Data, and Meteorological Forecasts"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6287639\/10820123\/11236080.pdf?arnumber=11236080","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T06:07:39Z","timestamp":1763618859000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11236080\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":62,"URL":"https:\/\/doi.org\/10.1109\/access.2025.3630992","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]}}}