{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T15:23:17Z","timestamp":1786116197670,"version":"build-2736575974"},"reference-count":26,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T00:00:00Z","timestamp":1781481600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T00:00:00Z","timestamp":1781481600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100007382","name":"Universidade Federal Do Par\u00e1","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100007382","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Computing"],"published-print":{"date-parts":[[2026,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    This paper introduces a fast-converging learning framework for residential energy consumption forecasting, named Federated Decision Tree (FEDT). FEDT integrates Federated Learning (FL) with Internet of Things (IoT) infrastructure and employs decision tree models for decentralized training across heterogeneous edge devices. The framework incorporates one client-side training strategy and four server-side aggregation mechanisms to collaboratively construct a global decision tree model. FEDT was fully implemented and evaluated on diverse platforms, including Raspberry Pi, Android smartphones, and personal computers (PCs). Experimental results show that FEDT stabilizes by the\n                    <jats:inline-formula>\n                      <jats:tex-math>$$17^{\\text {th}}$$<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    round with approximately 80 trees per client, outperforming FedAVG, which requires 38 rounds to reach comparable performance. The fast-converging global training achieves forecasting errors below 2% across all clients. Additionally, a scalability analysis demonstrates that FEDT maintains efficient training and inference times, satisfactory server-side aggregation time and local client training time, and amount of communication traffic.\n                  <\/jats:p>","DOI":"10.1007\/s00607-026-01685-2","type":"journal-article","created":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T10:46:20Z","timestamp":1781520380000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Fast-converging federated decision trees for smart-home energy consumption prediction"],"prefix":"10.1007","volume":"108","author":[{"given":"J\u00falio","family":"Oliveira","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuri","family":"Santo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andr\u00e9","family":"Riker","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eirini Eleni","family":"Tsiropoulou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Glaucio H. S.","family":"Carvalho","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,15]]},"reference":[{"key":"1685_CR1","unstructured":"U.S. energy information administration: international energy outlook 2024\u2013narrative. Accessed: 26 April 2025 (2024). https:\/\/www.eia.gov\/outlooks\/ieo\/narrative\/index.php"},{"key":"1685_CR2","doi-asserted-by":"publisher","first-page":"71555","DOI":"10.1109\/ACCESS.2024.3400972","volume":"12","author":"M-P Wu","year":"2024","unstructured":"Wu M-P, Wu F (2024) Predicting residential electricity consumption using cnn-bilstm-sa neural networks. IEEE Access 12:71555\u201371565. https:\/\/doi.org\/10.1109\/ACCESS.2024.3400972","journal-title":"IEEE Access"},{"issue":"1","key":"1685_CR3","doi-asserted-by":"publisher","first-page":"488","DOI":"10.1109\/TII.2020.3035451","volume":"18","author":"S Lee","year":"2022","unstructured":"Lee S, Choi D-H (2022) Federated reinforcement learning for energy management of multiple smart homes with distributed energy resources. IEEE Trans Ind Inform 18(1):488\u2013497. https:\/\/doi.org\/10.1109\/TII.2020.3035451","journal-title":"IEEE Trans Ind Inform"},{"key":"1685_CR4","doi-asserted-by":"crossref","unstructured":"Li Y, Yao R, Qin D, Wang Y (2024) Lightweight federated learning for on-device non-intrusive load monitoring. IEEE transactions on smart grid","DOI":"10.1109\/TSG.2024.3482363"},{"key":"1685_CR5","doi-asserted-by":"publisher","first-page":"127943","DOI":"10.1109\/ACCESS.2024.3440998","volume":"12","author":"SR Heiyanthuduwage","year":"2024","unstructured":"Heiyanthuduwage SR, Altas I, Bewong M, Islam MZ, Deho OB (2024) Decision trees in federated learning: current state and future opportunities. IEEE Access 12:127943\u2013127965. https:\/\/doi.org\/10.1109\/ACCESS.2024.3440998","journal-title":"IEEE Access"},{"issue":"21","key":"1685_CR6","doi-asserted-by":"publisher","first-page":"20889","DOI":"10.1109\/JIOT.2022.3176469","volume":"9","author":"D Wu","year":"2022","unstructured":"Wu D, Ullah R, Harvey P, Kilpatrick P, Spence I, Varghese B (2022) Fedadapt: adaptive offloading for iot devices in federated learning. IEEE Internet Things J 9(21):20889\u201320901. https:\/\/doi.org\/10.1109\/JIOT.2022.3176469","journal-title":"IEEE Internet Things J"},{"key":"1685_CR7","doi-asserted-by":"crossref","unstructured":"Barbosa L, Santo Y, Oliveira J, Astudillo CA, Cordeiro W, Riker A, Carvalho GHS (2025) Federated learning of decision trees in cooperative iot edge computing. 30th IEEE symposium on computers and communications (ISCC)","DOI":"10.1109\/ISCC65549.2025.11325946"},{"key":"1685_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.106654","volume":"124","author":"M Al-Quraan","year":"2023","unstructured":"Al-Quraan M, Khan A, Centeno A, Zoha A, Imran MA, Mohjazi L (2023) Fedratrees: a novel computation-communication efficient federated learning framework investigated in smart grids. Eng Appl Artif Intell 124:106654","journal-title":"Eng Appl Artif Intell"},{"key":"1685_CR9","doi-asserted-by":"crossref","unstructured":"Barja-Martinez S, Teng F, Junyent-Ferr\u00e9 A, Arag\u00fc\u00e9s-Pe\u00f1alba M (2024) Personalized federated learning with cost-oriented load forecasting for home energy management systems. IEEE Trans Ind Appl","DOI":"10.1109\/TIA.2024.3462668"},{"key":"1685_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijepes.2021.107669","volume":"137","author":"MN Fekri","year":"2022","unstructured":"Fekri MN, Grolinger K, Mir S (2022) Distributed load forecasting using smart meter data: federated learning with recurrent neural networks. Int J Electr Power Energy Syst 137:107669","journal-title":"Int J Electr Power Energy Syst"},{"key":"1685_CR11","unstructured":"Li Q, Zhaomin W, Cai Y, Yung CM, Fu T, He B (2023) Fedtree: a federated learning system for trees. Proceedings of machine learning and systems 5"},{"issue":"1","key":"1685_CR12","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1109\/LES.2022.3207968","volume":"16","author":"T Shen","year":"2022","unstructured":"Shen T, Mishra CS, Sampson J, Kandemir MT, Narayanan V (2022) An efficient edge-cloud partitioning of random forests for distributed sensor networks. IEEE Embed Syst Lett 16(1):21\u201324","journal-title":"IEEE Embed Syst Lett"},{"key":"1685_CR13","doi-asserted-by":"crossref","unstructured":"Souza LAC, Rebello GAF, Camilo GF, Guimar\u00e3es LC, Duarte OCM (2020) Dfedforest: decentralized federated forest. In: 2020 IEEE international conference on blockchain (blockchain). IEEE, pp 90\u201397","DOI":"10.1109\/Blockchain50366.2020.00019"},{"key":"1685_CR14","doi-asserted-by":"publisher","DOI":"10.1109\/tai.2024.3433419","author":"S Zhao","year":"2024","unstructured":"Zhao S, Zhu Z, Li X, Chen Y-C (2024) Communication-efficient federated learning for decision trees. IEEE Trans Artif Intell. https:\/\/doi.org\/10.1109\/tai.2024.3433419","journal-title":"IEEE Trans Artif Intell"},{"issue":"3","key":"1685_CR15","doi-asserted-by":"publisher","first-page":"843","DOI":"10.1109\/TBDATA.2020.2992755","volume":"8","author":"Y Liu","year":"2020","unstructured":"Liu Y, Liu Y, Liu Z, Liang Y, Meng C, Zhang J, Zheng Y (2020) Federated forest. IEEE Transactions on Big Data 8(3):843\u2013854","journal-title":"IEEE Transactions on Big Data"},{"issue":"6","key":"1685_CR16","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1109\/MIS.2021.3082561","volume":"36","author":"K Cheng","year":"2021","unstructured":"Cheng K, Fan T, Jin Y, Liu Y, Chen T, Papadopoulos D, Yang Q (2021) Secureboost: a lossless federated learning framework. IEEE Intell Syst 36(6):87\u201398","journal-title":"IEEE Intell Syst"},{"key":"1685_CR17","doi-asserted-by":"crossref","unstructured":"Feng Z, Xiong H, Song C, Yang S, Zhao B, Wang L, Chen Z, Yang S, Liu L, Huan J (2019) Securegbm: secure multi-party gradient boosting. In: 2019 IEEE international conference on big data (big data), IEEE. pp. 1312\u20131321","DOI":"10.1109\/BigData47090.2019.9006000"},{"key":"1685_CR18","doi-asserted-by":"crossref","unstructured":"Chen X, Zhou S, Guan B, Yang K, Fao H, Wang H, Wang Y (2021) Fed-eini: an efficient and interpretable inference framework for decision tree ensembles in vertical federated learning. In: 2021 IEEE international conference on big data (big data), IEEE\u00a0pp. 1242\u20131248","DOI":"10.1109\/BigData52589.2021.9671749"},{"key":"1685_CR19","doi-asserted-by":"publisher","first-page":"43954","DOI":"10.1109\/ACCESS.2022.3169502","volume":"10","author":"F Yamamoto","year":"2022","unstructured":"Yamamoto F, Ozawa S, Wang L (2022) efl-boost: efficient federated learning for gradient boosting decision trees. IEEE Access 10:43954\u201343963","journal-title":"IEEE Access"},{"key":"1685_CR20","doi-asserted-by":"publisher","unstructured":"Antonio Eng\u00a0Lim P, Hee\u00a0Park C (2024) A collaborative ensemble construction method for federated random forest. Expert Syst Appl 255(PD). https:\/\/doi.org\/10.1016\/j.eswa.2024.124742","DOI":"10.1016\/j.eswa.2024.124742"},{"key":"1685_CR21","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2025.3581295","author":"Y Zhang","year":"2025","unstructured":"Zhang Y, Liao H, Zhao L, Shao Y, Tian Z, Wang X, Meng D, Hou R (2025) An efficient speculative federated tree learning system with a lightweight nn-based predictor. IEEE Trans Parallel Distrib Syst. https:\/\/doi.org\/10.1109\/TPDS.2025.3581295","journal-title":"IEEE Trans Parallel Distrib Syst"},{"issue":"5","key":"1685_CR22","doi-asserted-by":"publisher","first-page":"3604","DOI":"10.1109\/TSC.2023.3279839","volume":"16","author":"Y Zheng","year":"2023","unstructured":"Zheng Y, Xu S, Wang S, Gao Y, Hua Z (2023) Privet: a privacy-preserving vertical federated learning service for gradient boosted decision tables. IEEE Trans Serv Comput 16(5):3604\u20133620","journal-title":"IEEE Trans Serv Comput"},{"key":"1685_CR23","first-page":"81","volume":"140","author":"LM Candanedo","year":"2017","unstructured":"Candanedo LM, Feldheim V, Deramaix D (2017) Data driven prediction models of energy use of appliances in a low-energy house. Energy\u00a0Build 140:81\u201397","journal-title":"Energy\u00a0Build"},{"key":"1685_CR24","doi-asserted-by":"publisher","unstructured":"Liu L, Zhang J, Song SH, Letaief KB (2020) Client-edge-cloud hierarchical federated learning. In: ICC 2020 - 2020 IEEE international conference on communications (ICC), pp. 1\u20136. https:\/\/doi.org\/10.1109\/ICC40277.2020.9148862","DOI":"10.1109\/ICC40277.2020.9148862"},{"issue":"10","key":"1685_CR25","doi-asserted-by":"publisher","first-page":"18449","DOI":"10.1109\/JIOT.2024.3362972","volume":"11","author":"T Zhang","year":"2024","unstructured":"Zhang T, Lam K-Y, Zhao J (2024) Device scheduling and assignment in hierarchical federated learning for internet of things. IEEE Internet Things J 11(10):18449\u201318462. https:\/\/doi.org\/10.1109\/JIOT.2024.3362972","journal-title":"IEEE Internet Things J"},{"issue":"3","key":"1685_CR26","doi-asserted-by":"publisher","first-page":"2217","DOI":"10.1109\/TNSE.2025.3546333","volume":"12","author":"Z Qin","year":"2025","unstructured":"Qin Z, Feng G, Liu Y, Yum TP, Wang F, Wang J (2025) Efficient federated learning in wireless networks with incremental model quantization and uploading. IEEE Trans Netw Sci Eng 12(3):2217\u20132230. https:\/\/doi.org\/10.1109\/TNSE.2025.3546333","journal-title":"IEEE Trans Netw Sci Eng"}],"container-title":["Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00607-026-01685-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00607-026-01685-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00607-026-01685-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T08:03:33Z","timestamp":1786003413000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00607-026-01685-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,15]]},"references-count":26,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2026,7]]}},"alternative-id":["1685"],"URL":"https:\/\/doi.org\/10.1007\/s00607-026-01685-2","relation":{},"ISSN":["0010-485X","1436-5057"],"issn-type":[{"value":"0010-485X","type":"print"},{"value":"1436-5057","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,15]]},"assertion":[{"value":"30 January 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 May 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 June 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no conflict of interest.","order":1,"name":"Ethics","label":"Conflict of interest","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"97"}}