{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T16:59:15Z","timestamp":1783184355358,"version":"3.54.6"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T00:00:00Z","timestamp":1710288000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T00:00:00Z","timestamp":1710288000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"\u897f\u4eac\u5927\u5b66\uff0c\u4e2d\u56fd","award":["XJ22B04"],"award-info":[{"award-number":["XJ22B04"]}]},{"name":"\u897f\u4eac\u5927\u5b66\uff0c\u4e2d\u56fd","award":["XJ22B04"],"award-info":[{"award-number":["XJ22B04"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Cloud Comp"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>With the rapid growth of Internet of Vehicles (IoV) technology, the performance and privacy of IoV terminals (IoVT) have become increasingly important. This paper proposes a federated learning model for IoVT classification using connection records (FLM-ICR) to address privacy concerns and poor computational performance in analyzing users' private data in IoV. FLM-ICR, in the horizontally federated learning client-server architecture, utilizes an improved multi-layer perceptron and logistic regression network as the model backbone, employs the federated momentum gradient algorithm as the local model training optimizer, and uses the federated Gaussian differential privacy algorithm to protect the security of the computation process. The experiment evaluates the model's classification performance using the confusion matrix, explores the impact of client collaboration on model performance, demonstrates the model's suitability for imbalanced data distribution, and confirms the effectiveness of federated learning for model training. FLM-ICR achieves the accuracy, precision, recall, specificity, and F1 score of 0.795, 0.735, 0.835, 0.75, and 0.782, respectively, outperforming existing research methods and balancing classification performance and privacy security, making it suitable for IoV computation and analysis of private data.<\/jats:p>","DOI":"10.1186\/s13677-024-00623-x","type":"journal-article","created":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T21:31:39Z","timestamp":1710279099000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["FLM-ICR: a federated learning model for classification of internet of vehicle terminals using connection records"],"prefix":"10.1186","volume":"13","author":[{"given":"Kai","family":"Yang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiawei","family":"Du","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingchao","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feng","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ye","family":"Tang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ming","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhibin","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,3,13]]},"reference":[{"issue":"2","key":"623_CR1","first-page":"2169","volume":"24","author":"L Liu","year":"2022","unstructured":"Liu L, Zhao M, Yu M, Jan MA, Lan D, Taherkordi A (2022) Mobility-aware multi-hop task offloading for autonomous driving in vehicular edge computing and networks. IEEE Trans Intell Transport Syst 24(2):2169\u20132182","journal-title":"IEEE Trans Intell Transport Syst"},{"key":"623_CR2","doi-asserted-by":"publisher","first-page":"644","DOI":"10.1016\/j.future.2017.12.003","volume":"92","author":"Q Kong","year":"2019","unstructured":"Kong Q, Lu R, Ma M, Bao H (2019) A privacy-preserving sensory data sharing scheme in Internet of Vehicles. Future Gener Comput Syst 92:644\u2013655","journal-title":"Future Gener Comput Syst"},{"key":"623_CR3","doi-asserted-by":"publisher","first-page":"108205","DOI":"10.1016\/j.compeleceng.2022.108205","volume":"102","author":"MS Rathore","year":"2022","unstructured":"Rathore MS, Poongodi M, Saurabh P, Lilhore UK, Bourouis S, Alhakami W, Osamor J, Hamdi M (2022) A novel trust-based security and privacy model for internet of vehicles using encryption and steganography. Comput Electrical Eng 102:108205","journal-title":"Comput Electrical Eng"},{"key":"623_CR4","doi-asserted-by":"publisher","first-page":"15513","DOI":"10.1109\/TITS.2023.3249745","volume":"24","author":"L Liu","year":"2023","unstructured":"Liu L, Feng J, Mu X, Pei Q, Lan D, Xiao M (2023) Asynchronous Deep Reinforcement Learning for Collaborative Task Computing and On-Demand Resource Allocation in Vehicular Edge Computing. IEEE Trans Intell TransportSyst 24:15513\u201315526","journal-title":"IEEE Trans Intell TransportSyst"},{"issue":"5","key":"623_CR5","doi-asserted-by":"publisher","first-page":"2685","DOI":"10.1109\/TNSE.2022.3168025","volume":"10","author":"Y Liu","year":"2023","unstructured":"Liu Y, Yu W, Ai Z, Xu G, Zhao L, Tian Z (2023) A blockchain-empowered federated learning in healthcare-based cyber physical systems. IEEE Trans Network Sci Eng 10(5):2685\u20132696","journal-title":"IEEE Trans Network Sci Eng"},{"key":"623_CR6","unstructured":"McMahan B, Moore E, Ramage D, Hampson S, y Arcas BA (2017) Communication-efficient learning of deep networks from decentralized data. 20th International Conference on Artificial Intelligence and Statistics. PMLR 54:1273\u20131282"},{"issue":"3","key":"623_CR7","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1109\/MCE.2019.2959108","volume":"9","author":"Z Li","year":"2020","unstructured":"Li Z, Sharma V, Mohanty SP (2020) Preserving data privacy via federated learning: challenges and solutions. IEEE Consum Electron Mag 9(3):8\u201316","journal-title":"IEEE Consum Electron Mag"},{"key":"623_CR8","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1016\/j.comcom.2021.02.014","volume":"171","author":"MA Chamikara","year":"2021","unstructured":"Chamikara MA, Bertok P, Khalil I, Liu D, Camtepe S (2021) Privacy preserving distributed machine learning with federated learning. Comput Commun 171:112\u2013125","journal-title":"Comput Commun"},{"key":"623_CR9","doi-asserted-by":"publisher","first-page":"1446","DOI":"10.1109\/TNSM.2023.3280515","volume":"20","author":"L Liu","year":"2023","unstructured":"Liu L, Tian Y, Chakraborty C, Feng J, Pei Q, Zhen L, Yu K (2023) Multilevel federated learning based intelligent traffic flow forecasting for transportation network management. IEEE Trans Netw Serv Manag 20:1446\u20131458","journal-title":"IEEE Trans Netw Serv Manag"},{"key":"623_CR10","doi-asserted-by":"publisher","unstructured":"Xiang T, Bi Y, Chen X, Liu Y, Wang B, Shen X, Wang X (2023) Federated Learning with Dynamic Epoch Adjustment and Collaborative Training in Mobile Edge Computing. IEEE Transactions on Mobile Computing, vol 01, pp. 1\u201316. https:\/\/doi.org\/10.1109\/TMC.2023.3288392","DOI":"10.1109\/TMC.2023.3288392"},{"issue":"11","key":"623_CR11","doi-asserted-by":"publisher","first-page":"8391","DOI":"10.1007\/s11227-019-03104-0","volume":"76","author":"P Zhao","year":"2020","unstructured":"Zhao P, Zhang G, Wan S, Liu G, Umer T (2020) A survey of local differential privacy for securing internet of vehicles. J Supercomput 76(11):8391\u20138412","journal-title":"J Supercomput"},{"key":"623_CR12","first-page":"1","volume-title":"Proceedings of the second workshop on distributed infrastructures for deep learning","author":"A Nilsson","year":"2018","unstructured":"Nilsson A, Smith S, Ulm G, Gustavsson E, Jirstrand M (2018) A performance evaluation of federated learning algorithms. Proceedings of the second workshop on distributed infrastructures for deep learning. ACM, New York, pp 1\u20138"},{"key":"623_CR13","first-page":"965","volume-title":"IEEE 38th International Conference on Data Engineering","author":"Q Li","year":"2022","unstructured":"Li Q, Diao Y, Chen Q, He B (2022) Federated learning on non-iid data silos: An experimental study. IEEE 38th International Conference on Data Engineering. IEEE, Kuala Lumpur, pp 965\u2013978"},{"key":"623_CR14","doi-asserted-by":"publisher","first-page":"308","DOI":"10.1145\/2976749.2978318","volume-title":"Proceedings of the 2016 ACM SIGSAC conference on computer and communications security","author":"M Abadi","year":"2016","unstructured":"Abadi M, Chu A, Goodfellow , I, McMahan HB, Mironov I, Talwar K, Zhang L (2016) Deep learning with differential privacy. Proceedings of the 2016 ACM SIGSAC conference on computer and communications security. ACM, New York, pp 308\u2013318"},{"issue":"7","key":"623_CR15","doi-asserted-by":"publisher","first-page":"5827","DOI":"10.1109\/JIOT.2019.2952146","volume":"7","author":"PC Arachchige","year":"2019","unstructured":"Arachchige PC, Bertok P, Khalil I, Liu D, Camtepe S, Atiquzzaman M (2019) Local differential privacy for deep learning. IEEE Internet Things J 7(7):5827\u20135842","journal-title":"IEEE Internet Things J"},{"key":"623_CR16","unstructured":"Choudhury O, Gkoulalas-Divanis A, Salonidis T, Sylla I, Park Y, Hsu G, Das A (2019) Differential privacy-enabled federated learning for sensitive health data. arXiv preprint arXiv:1910.02578"},{"key":"623_CR17","first-page":"484","volume-title":"IEEE 6th International Conference on Computer and Communication Systems","author":"G Yang","year":"2021","unstructured":"Yang G, Wang S, Wang H (2021) Federated learning with personalized local differential privacy. IEEE 6th International Conference on Computer and Communication Systems. IEEE, Chengdu, pp 484\u2013489"},{"issue":"4","key":"623_CR18","doi-asserted-by":"publisher","first-page":"4298","DOI":"10.1109\/TVT.2020.2973651","volume":"69","author":"Y Lu","year":"2020","unstructured":"Lu Y, Huang X, Zhang K, Maharjan S, Zhang Y (2020) Blockchain empowered asynchronous federated learning for secure data sharing in internet of vehicles. IEEE Trans Veh Technol 69(4):4298\u20134311","journal-title":"IEEE Trans Veh Technol"},{"issue":"9","key":"623_CR19","doi-asserted-by":"publisher","first-page":"7737","DOI":"10.1109\/JIOT.2022.3230412","volume":"10","author":"Z Yang","year":"2022","unstructured":"Yang Z, Zhang X, Wu D, Wang R, Zhang P, Wu Y (2022) Efficient Asynchronous Federated Learning Research in the Internet of Vehicles. IEEE Internet Things J 10(9):7737\u20137748","journal-title":"IEEE Internet Things J"},{"issue":"7","key":"623_CR20","doi-asserted-by":"publisher","first-page":"7385","DOI":"10.1109\/JSEN.2022.3153338","volume":"22","author":"P Zhao","year":"2022","unstructured":"Zhao P, Huang Y, Gao J, Xing L, Wu H, Ma H (2022) Federated learning-based collaborative authentication protocol for shared data in social IoV. IEEE Sensors J 22(7):7385\u20137398","journal-title":"IEEE Sensors J"},{"key":"623_CR21","first-page":"012007","volume-title":"IOP Conference Series: Earth and Environmental Science","author":"X Luo","year":"2020","unstructured":"Luo X, Wang J, Xu J, Shen M (2020) Research on Data Privacy Protection of Internet of Vehicles Based on Differential Privacy. IOP Conference Series: Earth and Environmental Science. IOP Publishing, Guangzhou, p 012007"},{"key":"623_CR22","doi-asserted-by":"crossref","unstructured":"Bakopoulou E, Tillman B, Markopoulou A (2021) Fedpacket: A federated learning approach to mobile packet classification. IEEE Trans Mobile Comput 21(10):3609\u20133628","DOI":"10.1109\/TMC.2021.3058627"},{"key":"623_CR23","doi-asserted-by":"publisher","first-page":"371","DOI":"10.1016\/j.neucom.2021.07.098","volume":"465","author":"H Zhu","year":"2021","unstructured":"Zhu H, Xu J, Liu S, Jin Y (2021) Federated learning on non-IID data: a survey. Neurocomputing 465:371\u2013390","journal-title":"Neurocomputing"},{"key":"623_CR24","doi-asserted-by":"publisher","first-page":"24462","DOI":"10.1109\/ACCESS.2021.3056919","volume":"9","author":"W Zhang","year":"2021","unstructured":"Zhang W, Wang X, Zhou P, Wu W, Zhang X (2021) Client selection for federated learning with non-iid data in mobile edge computing. IEEE Access 9:24462\u201324474","journal-title":"IEEE Access"},{"key":"623_CR25","unstructured":"Zhao Y, Li M, Lai L, Suda N, Civin D, Chandra V (2018) Federated learning with non-IID data arXiv:1806.00582. Available: https:\/\/arxiv.org\/abs\/1806.00582"},{"issue":"8","key":"623_CR26","doi-asserted-by":"publisher","first-page":"1754","DOI":"10.1109\/TPDS.2020.2975189","volume":"31","author":"W Liu","year":"2020","unstructured":"Liu W, Chen L, Chen Y, Zhang W (2020) Accelerating federated learning via momentum gradient descent. IEEE Trans Parallel Distrib Syst 31(8):1754\u20131766","journal-title":"IEEE Trans Parallel Distrib Syst"},{"key":"623_CR27","doi-asserted-by":"publisher","first-page":"1884","DOI":"10.1109\/TIFS.2023.3258255","volume":"18","author":"X Yuan","year":"2023","unstructured":"Yuan X, Ni W, Ding M, Wei K, Li J, Poor HV (2023) Amplitude-Varying Perturbation for Balancing Privacy and Utility in Federated Learning. IEEE Trans Inform Forensics Secur 18:1884\u20131897","journal-title":"IEEE Trans Inform Forensics Secur"},{"issue":"2","key":"623_CR28","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1109\/MIC.2021.3138853","volume":"26","author":"D He","year":"2021","unstructured":"He D, Du R, Zhu S, Zhang M, Liang K, Chan S (2021) Secure logistic regression for vertical federated learning. IEEE Internet Comput 26(2):61\u201368","journal-title":"IEEE Internet Comput"},{"key":"623_CR29","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1007\/978-3-030-63076-8_10","volume":"12500","author":"S Wei","year":"2020","unstructured":"Wei S, Tong Y, Zhou Z, Song T (2020) Efficient and fair data valuation for horizontal federated learning. Federated Learn 12500:139\u2013152","journal-title":"Federated Learn"},{"key":"623_CR30","first-page":"389","volume-title":"21st Asia-Pacific Network Operations and Management Symposium","author":"U Majeed","year":"2020","unstructured":"Majeed U, Khan LU, Hong CS (2020) Cross-silo horizontal federated learning for flow-based time-related-features oriented traffic classification. 21st Asia-Pacific Network Operations and Management Symposium. IEEE, Daegu, pp 389\u2013392"},{"key":"623_CR31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.neucom.2021.10.027","volume":"468","author":"B Xin","year":"2022","unstructured":"Xin B, Geng Y, Hu T, Chen S, Yang W, Wang S, Huang L (2022) Federated synthetic data generation with differential privacy. Neurocomputing 468:1\u201310","journal-title":"Neurocomputing"},{"key":"623_CR32","doi-asserted-by":"publisher","first-page":"102418","DOI":"10.1016\/j.sysarc.2022.102418","volume":"124","author":"W Liu","year":"2022","unstructured":"Liu W, Cheng J, Wang X, Lu X, Yin J (2022) Hybrid differential privacy based federated learning for Internet of Things. J Syst Architect 124:102418","journal-title":"J Syst Architect"},{"key":"623_CR33","doi-asserted-by":"publisher","first-page":"22359","DOI":"10.1109\/ACCESS.2022.3151670","volume":"10","author":"A El Ouadrhiri","year":"2022","unstructured":"El Ouadrhiri A, Abdelhadi A (2022) Differential privacy for deep and federated learning: a survey. IEEE Access 10:22359\u201322380","journal-title":"IEEE Access"},{"key":"623_CR34","doi-asserted-by":"publisher","first-page":"24808","DOI":"10.1109\/ACCESS.2023.3254915","volume":"11","author":"J Du","year":"2023","unstructured":"Du J, Yang K, Hu Y, Jiang L (2023) Nids-cnnlstm: Network intrusion detection classification model based on deep learning. IEEE Access 11:24808\u201324821","journal-title":"IEEE Access"}],"updated-by":[{"DOI":"10.1186\/s13677-024-00638-4","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2024,3,26]],"date-time":"2024-03-26T00:00:00Z","timestamp":1711411200000}}],"container-title":["Journal of Cloud Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13677-024-00623-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13677-024-00623-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13677-024-00623-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,26]],"date-time":"2024-03-26T18:01:42Z","timestamp":1711476102000},"score":1,"resource":{"primary":{"URL":"https:\/\/journalofcloudcomputing.springeropen.com\/articles\/10.1186\/s13677-024-00623-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,13]]},"references-count":34,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["623"],"URL":"https:\/\/doi.org\/10.1186\/s13677-024-00623-x","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-3775224\/v1","asserted-by":"object"}]},"ISSN":["2192-113X"],"issn-type":[{"value":"2192-113X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,13]]},"assertion":[{"value":"19 December 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 March 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 March 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 March 2024","order":4,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Correction","order":5,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"A Correction to this paper has been published:","order":6,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"https:\/\/doi.org\/10.1186\/s13677-024-00638-4","URL":"https:\/\/doi.org\/10.1186\/s13677-024-00638-4","order":7,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"57"}}