{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T15:32:37Z","timestamp":1772983957673,"version":"3.50.1"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2025,7,7]],"date-time":"2025-07-07T00:00:00Z","timestamp":1751846400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,7,7]],"date-time":"2025-07-07T00:00:00Z","timestamp":1751846400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62072184"],"award-info":[{"award-number":["62072184"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Complex Intell. Syst."],"published-print":{"date-parts":[[2025,9]]},"DOI":"10.1007\/s40747-025-02002-8","type":"journal-article","created":{"date-parts":[[2025,7,7]],"date-time":"2025-07-07T03:23:33Z","timestamp":1751858613000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Privacy-aware electric vehicle load forecasting via blockchain-based federated transfer learning"],"prefix":"10.1007","volume":"11","author":[{"given":"Ruochen","family":"Jin","sequence":"first","affiliation":[]},{"given":"Hong","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,7,7]]},"reference":[{"key":"2002_CR1","doi-asserted-by":"publisher","DOI":"10.1109\/TIV.2022.3168577","author":"Y Li","year":"2022","unstructured":"Li Y, He S, Li Y, Ge L, Lou S, Zeng Z (2022) Probabilistic charging power forecast of evcs: Reinforcement learning assisted deep learning approach. IEEE Trans Intell Veh. https:\/\/doi.org\/10.1109\/TIV.2022.3168577","journal-title":"IEEE Trans Intell Veh"},{"key":"2002_CR2","unstructured":"Global EV Outlook 2021 (2021). https:\/\/www.iea.org\/reports\/global-ev-outlook-2021"},{"key":"2002_CR3","doi-asserted-by":"publisher","first-page":"266","DOI":"10.1016\/j.enconman.2017.12.053","volume":"158","author":"A Garc\u00eda-Olivares","year":"2018","unstructured":"Garc\u00eda-Olivares A, Sol\u00e9 J, Osychenko O (2018) Transportation in a 100% renewable energy system. Energy Convers Manage 158:266\u2013285","journal-title":"Energy Convers Manage"},{"issue":"2","key":"2002_CR4","first-page":"8","volume":"35","author":"S Nandy","year":"2015","unstructured":"Nandy S (2015) Urbanization in india\u2013past, present and future consequences. Urban India 35(2):8\u201324","journal-title":"Urban India"},{"issue":"3","key":"2002_CR5","first-page":"2156","volume":"14","author":"Y Zhang","year":"2023","unstructured":"Zhang Y et al (2023) Privacy-preserving federated learning for electric vehicle load forecasting: A blockchain-based approach. IEEE Trans Smart Grid 14(3):2156\u20132168","journal-title":"IEEE Trans Smart Grid"},{"key":"2002_CR6","doi-asserted-by":"publisher","first-page":"114973","DOI":"10.1016\/j.apenergy.2020.114973","volume":"268","author":"C Crozier","year":"2020","unstructured":"Crozier C, Morstyn T, McCulloch M (2020) The opportunity for smart charging to mitigate the impact of electric vehicles on transmission and distribution systems. Appl Energy 268:114973","journal-title":"Appl Energy"},{"key":"2002_CR7","doi-asserted-by":"crossref","unstructured":"Wang H, Wang B, Fang C, Li W, Huang H. Charging load forecasting of electric vehicle based on charging frequency. In: IOP Conference Series: Earth and Environmental Science, vol. 237, p. 062008. IOP Publishing","DOI":"10.1088\/1755-1315\/237\/6\/062008"},{"issue":"2","key":"2002_CR8","doi-asserted-by":"publisher","first-page":"738","DOI":"10.1109\/TPWRS.2010.2052375","volume":"26","author":"D Wu","year":"2010","unstructured":"Wu D, Aliprantis DC, Gkritza K (2010) Electric energy and power consumption by light-duty plug-in electric vehicles. IEEE Trans Power Syst 26(2):738\u2013746","journal-title":"IEEE Trans Power Syst"},{"issue":"7","key":"2002_CR9","doi-asserted-by":"publisher","first-page":"1683","DOI":"10.1049\/iet-gtd.2016.1200","volume":"11","author":"T Yang","year":"2017","unstructured":"Yang T, Xu X, Guo Q, Zhang L, Sun H (2017) Ev charging behaviour analysis and modelling based on mobile crowdsensing data. IET Gener Trans Distrib 11(7):1683\u20131691","journal-title":"IET Gener Trans Distrib"},{"issue":"2","key":"2002_CR10","doi-asserted-by":"publisher","first-page":"741","DOI":"10.1109\/TSTE.2017.2759781","volume":"9","author":"MS Islam","year":"2017","unstructured":"Islam MS, Mithulananthan N, Hung DQ (2017) A day-ahead forecasting model for probabilistic ev charging loads at business premises. IEEE Trans Sustain Energy 9(2):741\u2013753","journal-title":"IEEE Trans Sustain Energy"},{"key":"2002_CR11","doi-asserted-by":"publisher","first-page":"116337","DOI":"10.1016\/j.apenergy.2020.116337","volume":"283","author":"L Buzna","year":"2021","unstructured":"Buzna L, De Falco P, Ferruzzi G, Khormali S, Proto D, Refa N, Straka M, Poel G (2021) An ensemble methodology for hierarchical probabilistic electric vehicle load forecasting at regular charging stations. Appl Energy 283:116337","journal-title":"Appl Energy"},{"issue":"4","key":"2002_CR12","doi-asserted-by":"publisher","first-page":"1909","DOI":"10.1109\/TPWRD.2014.2308990","volume":"29","author":"Q Dai","year":"2014","unstructured":"Dai Q, Cai T, Duan S, Zhao F (2014) Stochastic modeling and forecasting of load demand for electric bus battery-swap station. IEEE Trans Power Delivery 29(4):1909\u20131917","journal-title":"IEEE Trans Power Delivery"},{"key":"2002_CR13","doi-asserted-by":"crossref","first-page":"125801","DOI":"10.1016\/j.energy.2022.125801","volume":"263","author":"L Chen","year":"2023","unstructured":"Chen L et al (2023) Blockchain-enabled federated learning for privacy-preserving ev charging pattern analysis. Energy 263:125801","journal-title":"Energy"},{"issue":"3","key":"2002_CR14","doi-asserted-by":"publisher","first-page":"914","DOI":"10.1016\/j.ijforecast.2015.11.011","volume":"32","author":"T Hong","year":"2016","unstructured":"Hong T, Fan S (2016) Probabilistic electric load forecasting: A tutorial review. Int J Forecast 32(3):914\u2013938","journal-title":"Int J Forecast"},{"key":"2002_CR15","doi-asserted-by":"publisher","first-page":"1484","DOI":"10.1016\/j.rser.2017.05.212","volume":"81","author":"DW Meer","year":"2018","unstructured":"Meer DW, Wid\u00e9n J, Munkhammar J (2018) Review on probabilistic forecasting of photovoltaic power production and electricity consumption. Renew Sustain Energy Rev 81:1484\u20131512","journal-title":"Renew Sustain Energy Rev"},{"issue":"1","key":"2002_CR16","doi-asserted-by":"publisher","first-page":"404","DOI":"10.1109\/TSG.2011.2163650","volume":"3","author":"Q Gong","year":"2011","unstructured":"Gong Q, Midlam-Mohler S, Marano V, Rizzoni G (2011) Study of pev charging on residential distribution transformer life. IEEE Trans Smart Grid 3(1):404\u2013412","journal-title":"IEEE Trans Smart Grid"},{"issue":"1","key":"2002_CR17","doi-asserted-by":"publisher","first-page":"492","DOI":"10.1109\/TSG.2011.2172643","volume":"3","author":"G Li","year":"2012","unstructured":"Li G, Zhang X-P (2012) Modeling of plug-in hybrid electric vehicle charging demand in probabilistic power flow calculations. IEEE Trans Smart Grid 3(1):492\u2013499","journal-title":"IEEE Trans Smart Grid"},{"key":"2002_CR18","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1016\/j.trc.2012.04.002","volume":"25","author":"RN Fries","year":"2012","unstructured":"Fries RN, Gahrooei MR, Chowdhury M, Conway AJ (2012) Meeting privacy challenges while advancing intelligent transportation systems. Transport Res Part C Emerg Technol 25:34\u201345","journal-title":"Transport Res Part C Emerg Technol"},{"key":"2002_CR19","doi-asserted-by":"crossref","unstructured":"Jin R, Wei B, Luo Y, Ren T, Wu R (2021) Blockchain-based data collection with efficient anomaly detection for estimating battery state-of-health. IEEE Sensors J","DOI":"10.1109\/JSEN.2021.3066785"},{"key":"2002_CR20","unstructured":"Kone\u010dn\u00fd J, McMahan HB, Ramage D, Richt\u00e1rik P (2016) Federated optimization: Distributed machine learning for on-device intelligence. arXiv preprint arXiv:1610.02527"},{"key":"2002_CR21","doi-asserted-by":"crossref","unstructured":"Yang Z, Yang M, Zhang Y, Gu G, Ning P, Wang XS (2013 ) Appintent: Analyzing sensitive data transmission in android for privacy leakage detection. In: Proceedings of the ACM SIGSAC Conference on Computer & Communications Security, pp. 1043\u20131054","DOI":"10.1145\/2508859.2516676"},{"key":"2002_CR22","unstructured":"Goldreich O (1998) Secure multi-party computation. Manuscript. Preliminary version 78"},{"key":"2002_CR23","unstructured":"Bonawitz K, Ivanov V, Kreuter B, Marcedone A, McMahan HB, Patel S, Ramage D, Segal A, Seth K (2016) Practical secure aggregation for federated learning on user-held data. arXiv preprint arXiv:1611.04482"},{"issue":"3","key":"2002_CR24","doi-asserted-by":"publisher","first-page":"2054","DOI":"10.1109\/TII.2019.2930286","volume":"16","author":"T Wang","year":"2019","unstructured":"Wang T, Luo H, Jia W, Liu A, Xie M (2019) Mtes: An intelligent trust evaluation scheme in sensor-cloud-enabled industrial internet of things. IEEE Trans Industr Inf 16(3):2054\u20132062","journal-title":"IEEE Trans Industr Inf"},{"key":"2002_CR25","unstructured":"Passerat-Palmbach J, Farnan T, Miller R, Gross MS, Flannery HL, Gleim B (2019) A blockchain-orchestrated federated learning architecture for healthcare consortia. arXiv preprint arXiv:1910.12603"},{"key":"2002_CR26","unstructured":"Roy AG, Siddiqui S, P\u00f6lsterl S, Navab N, Wachinger C (2019) Braintorrent: A peer-to-peer environment for decentralized federated learning. arXiv preprint arXiv:1905.06731"},{"issue":"10","key":"2002_CR27","doi-asserted-by":"publisher","first-page":"12240","DOI":"10.1109\/TVT.2020.3018817","volume":"69","author":"W Sun","year":"2020","unstructured":"Sun W, Zhang H, Wang R, Zhang Y (2020) Reducing offloading latency for digital twin edge networks in 6g. IEEE Trans Veh Technol 69(10):12240\u201312251","journal-title":"IEEE Trans Veh Technol"},{"key":"2002_CR28","unstructured":"Wang X, et al (2024) Secure federated learning with compression-resistant watermarking. IEEE IoT J"},{"key":"2002_CR29","unstructured":"Anonymous: Persistverify: Federated ownership verification via spatial attention. arXiv preprint (2024)"},{"key":"2002_CR30","unstructured":"Anonymous: Securing ip in edge ai through multimodal watermarking. In: ACM CCS ( 2024)"},{"key":"2002_CR31","unstructured":"Anonymous: Verichroma: Rgb filter-based model verification. In: NeurIPS ( 2024)"},{"key":"2002_CR32","unstructured":"Anonymous: Fedlabx: Privacy-preserving federated framework. Complex Intell Syst (2024)"},{"key":"2002_CR33","unstructured":"Anonymous (2024) Fl-otcsenc: Secure learning via compressed sensing. IEEE TDSC"},{"key":"2002_CR34","doi-asserted-by":"crossref","unstructured":"Abbasi SF, et al ( 2024) Deep learning for synthetic skin lesion classification. In: Digital Health Innovations. IOS Press","DOI":"10.3233\/SHTI240612"},{"issue":"7","key":"2002_CR35","doi-asserted-by":"publisher","first-page":"5077","DOI":"10.1109\/TII.2020.3016025","volume":"17","author":"Y Yu","year":"2020","unstructured":"Yu Y, Liu S, Yeoh PL, Vucetic B, Li Y (2020) Layerchain: a hierarchical edge-cloud blockchain for large-scale low-delay industrial internet of things applications. IEEE Trans Industr Inf 17(7):5077\u20135086","journal-title":"IEEE Trans Industr Inf"},{"issue":"5","key":"2002_CR36","doi-asserted-by":"publisher","first-page":"568","DOI":"10.1016\/j.patrec.2011.11.023","volume":"33","author":"P Huang","year":"2012","unstructured":"Huang P, Wang G, Qin S (2012) Boosting for transfer learning from multiple data sources. Pattern Recogn Lett 33(5):568\u2013579","journal-title":"Pattern Recogn Lett"},{"issue":"4","key":"2002_CR37","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3369818","volume":"3","author":"X Qin","year":"2019","unstructured":"Qin X, Chen Y, Wang J, Yu C (2019) Cross-dataset activity recognition via adaptive spatial-temporal transfer learning. Proc ACM Interact Mobile Wearable Ubiquit Technol 3(4):1\u201325","journal-title":"Proc ACM Interact Mobile Wearable Ubiquit Technol"},{"key":"2002_CR38","first-page":"113176","volume":"177","author":"J Guo","year":"2023","unstructured":"Guo J et al (2023) Transfer learning-enhanced probabilistic forecasting of electric vehicle charging demand in smart grids. Renew Sustain Energy Rev 177:113176","journal-title":"Renew Sustain Energy Rev"},{"key":"2002_CR39","doi-asserted-by":"crossref","unstructured":"Majeed U, Hassan SS, Hong CS (2021 ) Cross-silo model-based secure federated transfer learning for flow-based traffic classification. In: International Conference on Information Networking (ICOIN), pp. 588\u2013593. IEEE","DOI":"10.1109\/ICOIN50884.2021.9333905"},{"key":"2002_CR40","first-page":"120305","volume":"331","author":"H Liu","year":"2023","unstructured":"Liu H et al (2023) Adaptive federated transfer learning for electric vehicle charging demand prediction under non-iid scenarios. Appl Energy 331:120305","journal-title":"Appl Energy"},{"key":"2002_CR41","unstructured":"McMahan HB, Moore E, Ramage D, Arcas BA (2016) Federated learning of deep networks using model averaging. arXiv preprint arXiv:1602.05629"},{"key":"2002_CR42","doi-asserted-by":"publisher","unstructured":"Paillier P (1999) Public-Key Cryptosystems Based on Composite Degree Residuosity Classes. In: Stern J (eds) Advances in cryptology \u2014 EUROCRYPT \u201999. EUROCRYPT 1999. Lecture notes in computer science, vol 1592. Springer, Berlin, Heidelberg. https:\/\/doi.org\/10.1007\/3-540-48910-X_16","DOI":"10.1007\/3-540-48910-X_16"},{"issue":"1","key":"2002_CR43","first-page":"452","volume":"11","author":"X Wang","year":"2024","unstructured":"Wang X et al (2024) Secure and efficient federated learning for ev charging load forecasting using homomorphic encryption. IEEE Internet Things J 11(1):452\u2013465","journal-title":"IEEE Internet Things J"},{"issue":"3","key":"2002_CR44","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1145\/3316481","volume":"52","author":"R Zhang","year":"2019","unstructured":"Zhang R, Xue R, Liu L (2019) Security and privacy on blockchain. ACM Comput Surv 52(3):51. https:\/\/doi.org\/10.1145\/3316481","journal-title":"ACM Comput Surv"},{"key":"2002_CR45","unstructured":"Go ethereum-official go implementation of the ethereum protocol (2021)"},{"key":"2002_CR46","unstructured":"ElaadNL Open Data (2020). https:\/\/platform.elaad.io\/analyses.html"},{"key":"2002_CR47","doi-asserted-by":"crossref","unstructured":"Bikcora C, Refa N, Verheijen L, Weiland S (2016 ) Prediction of availability and charging rate at charging stations for electric vehicles. In: International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), pp. 1\u20136. IEEE","DOI":"10.1109\/PMAPS.2016.7764216"},{"key":"2002_CR48","unstructured":"COROP regions. https:\/\/www.regioatlas.nl\/kaarten#_gemeenten"},{"key":"2002_CR49","unstructured":"Ryffel T, Trask A, Dahl M, Wagner B, Mancuso J, Rueckert D, Passerat-Palmbach J (2018) A generic framework for privacy preserving deep learning. arXiv preprint arXiv:1811.04017"},{"key":"2002_CR50","first-page":"8026","volume":"32","author":"A Paszke","year":"2019","unstructured":"Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, Killeen T, Lin Z, Gimelshein N, Antiga L (2019) Pytorch: An imperative style, high-performance deep learning library. Adv Neural Inf Process Syst 32:8026\u20138037","journal-title":"Adv Neural Inf Process Syst"}],"container-title":["Complex & Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-025-02002-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40747-025-02002-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-025-02002-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,7]],"date-time":"2025-09-07T01:14:38Z","timestamp":1757207678000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40747-025-02002-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,7]]},"references-count":50,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2025,9]]}},"alternative-id":["2002"],"URL":"https:\/\/doi.org\/10.1007\/s40747-025-02002-8","relation":{},"ISSN":["2199-4536","2198-6053"],"issn-type":[{"value":"2199-4536","type":"print"},{"value":"2198-6053","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,7]]},"assertion":[{"value":"30 December 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 June 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 July 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declaration:"}},{"value":"The authors declare no Conflict of interest. This work was supported in part by the National Natural Science Foundation of China under Grant 62072184.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"376"}}