{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T15:10:54Z","timestamp":1772118654408,"version":"3.50.1"},"reference-count":24,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T00:00:00Z","timestamp":1763424000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T00:00:00Z","timestamp":1763424000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Internet Technology Letters"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>The advance of Internet\u2010of\u2010Things (IoT) devices in smart grids has enabled fine\u2010grained data collection for electric load prediction but raises concerns of data privacy and security. Traditional methods train a prediction model in a centralized way, which creates a vulnerability to privacy leakage and cyber\u2010attacks. In addition, the collected data by IoT devices often suffer from measurement noise and imbalanced patterns, which may degrade the performance of forecasting models. To address these challenges, we propose in this paper a novel approach, called Federated Electric Load Distribution Learning (FELDL), which integrates Label Distribution Learning (LDL) within a Federated Learning (FL) paradigm. FELDL generates a load distribution for each data point to model data noise and imbalance, and learns such distributions in an FL framework without centralizing data from multiple IoT devices. Finally, we evaluate the performance of FELDL on real\u2010world power consumption datasets, and the experimental results demonstrate that FELDL achieves competitive performance against the comparing methods. Overall, FELDL provides an effective and secure solution for accurate load prediction in IoT\u2010enabled smart grids.<\/jats:p>","DOI":"10.1002\/itl2.70177","type":"journal-article","created":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T13:22:35Z","timestamp":1763472155000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["<scp>IoT<\/scp>\n                    \u2010Enabled Electric Load Prediction via Federated Label Distribution Learning"],"prefix":"10.1002","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-2578-218X","authenticated-orcid":false,"given":"Binsheng","family":"Xi","sequence":"first","affiliation":[{"name":"State Grid Gansu Electric Power Company Lanzhou Power Supply Company  Lanzhou China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haiqiang","family":"Jin","sequence":"additional","affiliation":[{"name":"State Grid Gansu Electric Power Company Lanzhou Power Supply Company  Lanzhou China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kaibiao","family":"Li","sequence":"additional","affiliation":[{"name":"State Grid Gansu Electric Power Company Lanzhou Power Supply Company  Lanzhou China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fuquan","family":"Kui","sequence":"additional","affiliation":[{"name":"State Grid Gansu Electric Power Company Lanzhou Power Supply Company  Lanzhou China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,11,18]]},"reference":[{"key":"e_1_2_6_2_1","doi-asserted-by":"crossref","unstructured":"W.Jiang Y.Zhang H.Han et al. \u201cMulti\u2010Modal Big Data Analyzing Architecture for Industrial Internet of Things \u201dinProceedings of the 1st International Workshop on IoT Datasets for Multi\u2010Modal Large Model 2024 83\u201384.","DOI":"10.1145\/3698385.3699879"},{"key":"e_1_2_6_3_1","doi-asserted-by":"publisher","DOI":"10.1002\/itl2.39"},{"key":"e_1_2_6_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.egyr.2024.05.051"},{"key":"e_1_2_6_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.egyai.2024.100358"},{"key":"e_1_2_6_6_1","doi-asserted-by":"publisher","DOI":"10.3390\/asi3010005"},{"key":"e_1_2_6_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2025.126430"},{"key":"e_1_2_6_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCSS.2024.3496746"},{"key":"e_1_2_6_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11277-020-07649-9"},{"key":"e_1_2_6_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-023-06353-6"},{"key":"e_1_2_6_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2024.128019"},{"key":"e_1_2_6_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2023.102189"},{"key":"e_1_2_6_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2021.3090430"},{"key":"e_1_2_6_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3110784"},{"key":"e_1_2_6_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2023.3312118"},{"key":"e_1_2_6_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCE.2024.3368087"},{"key":"e_1_2_6_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2016.2545658"},{"key":"e_1_2_6_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2023.3341807"},{"key":"e_1_2_6_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2025.3581552"},{"key":"e_1_2_6_20_1","doi-asserted-by":"crossref","unstructured":"Y.LuandX.Jia \u201cPredicting Label Distribution From Ternary Labels \u201din Proceedings of the 38th International Conference on Neural Information Processing Systems 2025 70431\u201370452.","DOI":"10.52202\/079017-2251"},{"key":"e_1_2_6_21_1","doi-asserted-by":"crossref","unstructured":"A.SalamandA. 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