更正:FLM-ICR:利用连接记录对车载互联网终端进行分类的联合学习模型

Kai Yang, Jiawei Du, Jingchao Liu, Feng Xu, Ye Tang, Ming Liu, Zhibin Li
{"title":"更正:FLM-ICR:利用连接记录对车载互联网终端进行分类的联合学习模型","authors":"Kai Yang, Jiawei Du, Jingchao Liu, Feng Xu, Ye Tang, Ming Liu, Zhibin Li","doi":"10.1186/s13677-024-00638-4","DOIUrl":null,"url":null,"abstract":"<p><b>Correction: Journal of Cloud Computing (2024) 13:57 </b></p><p>https://doi.org/10.1186/s13677-024-00623-x</p><p>Following publication of the original article [1], we have been notified that there is duplicate of the body text in the published article.</p><p>Now the text is:</p><p>MLP ((model): Sequential ((0): Linear (in_features=3, out_features=200, bias=True)</p><ol>\n<li>\n<span>1.</span>\n<p>Dropout (p=0.2, inplace=False)</p>\n</li>\n<li>\n<span>2.</span>\n<p>ReLU ()</p>\n</li>\n<li>\n<span>3.</span>\n<p>Linear (in_features=200, out_features=2, bias=True)))</p>\n</li>\n</ol><p>The improved MLP comprises linear layers, Dropout, and the ReLU activation function. This architecture is established using the Sequential class to construct a feedforward neural network for sample classification.</p><p>Initially, the linear layer conducts linear transformations to augment the feature information of the samples, with an input dimension of 2 and an output dimension of 200. Dropout is then implemented with a probability of 0.2 for random Dropout, mitigating overfitting. Subsequently, the ReLU non-linear activation function is employed to enhance the network?s non-linear expressive capability. Finally, the linear layer is utilized for dimension reduction and classification purposes.</p><p>MLP ((model): Sequential ((0): Linear (in_features=3, out_features=200, bias=True)</p><ol>\n<li>\n<span>1.</span>\n<p>Dropout (p=0.2, inplace=False)</p>\n</li>\n<li>\n<span>2.</span>\n<p>ReLU ()</p>\n</li>\n<li>\n<span>3.</span>\n<p>Linear (in_features=200, out_features=2, bias=True)))</p>\n</li>\n</ol><p>It should be:</p><p>MLP ((model): Sequential ((0): Linear (in_features=3, out_features=200, bias=True)</p><ol>\n<li>\n<span>1.</span>\n<p>Dropout (p=0.2, inplace=False)</p>\n</li>\n<li>\n<span>2.</span>\n<p>ReLU ()</p>\n</li>\n<li>\n<span>3.</span>\n<p>Linear (in_features=200, out_features=2, bias=True)))</p>\n</li>\n</ol><p>The improved MLP comprises linear layers, Dropout, and the ReLU activation function. This architecture is established using the Sequential class to construct a feedforward neural network for sample classification.</p><p>Initially, the linear layer conducts linear transformations to augment the feature information of the samples, with an input dimension of 2 and an output dimension of 200. Dropout is then implemented with a probability of 0.2 for random Dropout, mitigating overfitting. Subsequently, the ReLU non-linear activation function is employed to enhance the network?s non-linear expressive capability. Finally, the linear layer is utilized for dimension reduction and classification purposes.</p><p>The original article was updated.</p><ol data-track-component=\"outbound reference\"><li data-counter=\"1.\"><p>Yang et al (2024) FLM-ICR: a federated learning model for classification of internet of vehicle terminals using connection records. 13:57 https://doi.org/10.1186/s13677-024-00623-x</p></li></ol><p>Download references<svg aria-hidden=\"true\" focusable=\"false\" height=\"16\" role=\"img\" width=\"16\"><use xlink:href=\"#icon-eds-i-download-medium\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"></use></svg></p><h3>Authors and Affiliations</h3><ol><li><p>School of Computer Science, Xijing University, 710123, Xi’an, China</p><p>Kai Yang, Jiawei Du &amp; Jingchao Liu</p></li><li><p>Guangzhou Institute of Technology, Xidian University, 510555, Guangzhou, China</p><p>Feng Xu</p></li><li><p>Beijing Special Electromechanical Research Institute, 100020, Beijing, China</p><p>Ye Tang</p></li><li><p>The 7th Research Institute of China Electronics Technology Group Corporation, 510310, Guangzhou, China</p><p>Ming Liu &amp; Zhibin Li</p></li></ol><span>Authors</span><ol><li><span>Kai Yang</span>View author publications<p>You can also search for this author in <span>PubMed<span> </span>Google Scholar</span></p></li><li><span>Jiawei Du</span>View author publications<p>You can also search for this author in <span>PubMed<span> </span>Google Scholar</span></p></li><li><span>Jingchao Liu</span>View author publications<p>You can also search for this author in <span>PubMed<span> </span>Google Scholar</span></p></li><li><span>Feng Xu</span>View author publications<p>You can also search for this author in <span>PubMed<span> </span>Google Scholar</span></p></li><li><span>Ye Tang</span>View author publications<p>You can also search for this author in <span>PubMed<span> </span>Google Scholar</span></p></li><li><span>Ming Liu</span>View author publications<p>You can also search for this author in <span>PubMed<span> </span>Google Scholar</span></p></li><li><span>Zhibin Li</span>View author publications<p>You can also search for this author in <span>PubMed<span> </span>Google Scholar</span></p></li></ol><h3>Corresponding author</h3><p>Correspondence to Jiawei Du.</p><h3>Publisher’s Note</h3><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p><p>The online version of the original article can be found at https://doi.org/10.1186/s13677-024-00623-x</p><p><b>Open Access</b> This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.</p>\n<p>Reprints and permissions</p><img alt=\"Check for updates. 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0-.92-.08-1.33-.25-.41-.16-.77-.4-1.08-.7-.3-.31-.54-.69-.72-1.13-.17-.44-.26-.95-.26-1.52zm4.61-.62c0-.55-.11-.98-.34-1.28-.23-.31-.58-.47-1.06-.47-.41 0-.77.15-1.08.45-.31.29-.5.73-.57 1.3zm3.01 2.23c.31.24.61.43.92.57.3.13.63.2.98.2.38 0 .65-.08.83-.23s.27-.35.27-.6c0-.14-.05-.26-.13-.37-.08-.1-.2-.2-.34-.28-.14-.09-.29-.16-.47-.23l-.53-.22c-.23-.09-.46-.18-.69-.3-.23-.11-.44-.24-.62-.4s-.33-.35-.45-.55c-.12-.21-.18-.46-.18-.75 0-.61.23-1.1.68-1.49.44-.38 1.06-.57 1.83-.57.48 0 .91.08 1.29.25s.71.36.99.57l-.74.98c-.24-.17-.49-.32-.73-.42-.25-.11-.51-.16-.78-.16-.35 0-.6.07-.76.21-.17.15-.25.33-.25.54 0 .14.04.26.12.36s.18.18.31.26c.14.07.29.14.46.21l.54.19c.23.09.47.18.7.29s.44.24.64.4c.19.16.34.35.46.58.11.23.17.5.17.82 0 .3-.06.58-.17.83-.12.26-.29.48-.51.68-.23.19-.51.34-.84.45-.34.11-.72.17-1.15.17-.48 0-.95-.09-1.41-.27-.46-.19-.86-.41-1.2-.68z" fill="#535353"/></g></svg>\" width=\"57\"/><h3>Cite this article</h3><p>Yang, K., Du, J., Liu, J. <i>et al.</i> Correction: FLM-ICR: a federated learning model for classification of internet of vehicle terminals using connection records. <i>J Cloud Comp</i> <b>13</b>, 75 (2024). https://doi.org/10.1186/s13677-024-00638-4</p><p>Download citation<svg aria-hidden=\"true\" focusable=\"false\" height=\"16\" role=\"img\" width=\"16\"><use xlink:href=\"#icon-eds-i-download-medium\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"></use></svg></p><ul data-test=\"publication-history\"><li><p>Published<span>: </span><span><time datetime=\"2024-03-26\">26 March 2024</time></span></p></li><li><p>DOI</abbr><span>: </span><span>https://doi.org/10.1186/s13677-024-00638-4</span></p></li></ul><h3>Share this article</h3><p>Anyone you share the following link with will be able to read this content:</p><button data-track=\"click\" data-track-action=\"get shareable link\" data-track-external=\"\" data-track-label=\"button\" type=\"button\">Get shareable link</button><p>Sorry, a shareable link is not currently available for this article.</p><p data-track=\"click\" data-track-action=\"select share url\" data-track-label=\"button\"></p><button data-track=\"click\" data-track-action=\"copy share url\" data-track-external=\"\" data-track-label=\"button\" type=\"button\">Copy to clipboard</button><p> Provided by the Springer Nature SharedIt content-sharing initiative </p>","PeriodicalId":501257,"journal":{"name":"Journal of Cloud Computing","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Correction: FLM-ICR: a federated learning model for classification of internet of vehicle terminals using connection records\",\"authors\":\"Kai Yang, Jiawei Du, Jingchao Liu, Feng Xu, Ye Tang, Ming Liu, Zhibin Li\",\"doi\":\"10.1186/s13677-024-00638-4\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><b>Correction: Journal of Cloud Computing (2024) 13:57 </b></p><p>https://doi.org/10.1186/s13677-024-00623-x</p><p>Following publication of the original article [1], we have been notified that there is duplicate of the body text in the published article.</p><p>Now the text is:</p><p>MLP ((model): Sequential ((0): Linear (in_features=3, out_features=200, bias=True)</p><ol>\\n<li>\\n<span>1.</span>\\n<p>Dropout (p=0.2, inplace=False)</p>\\n</li>\\n<li>\\n<span>2.</span>\\n<p>ReLU ()</p>\\n</li>\\n<li>\\n<span>3.</span>\\n<p>Linear (in_features=200, out_features=2, bias=True)))</p>\\n</li>\\n</ol><p>The improved MLP comprises linear layers, Dropout, and the ReLU activation function. This architecture is established using the Sequential class to construct a feedforward neural network for sample classification.</p><p>Initially, the linear layer conducts linear transformations to augment the feature information of the samples, with an input dimension of 2 and an output dimension of 200. Dropout is then implemented with a probability of 0.2 for random Dropout, mitigating overfitting. Subsequently, the ReLU non-linear activation function is employed to enhance the network?s non-linear expressive capability. Finally, the linear layer is utilized for dimension reduction and classification purposes.</p><p>MLP ((model): Sequential ((0): Linear (in_features=3, out_features=200, bias=True)</p><ol>\\n<li>\\n<span>1.</span>\\n<p>Dropout (p=0.2, inplace=False)</p>\\n</li>\\n<li>\\n<span>2.</span>\\n<p>ReLU ()</p>\\n</li>\\n<li>\\n<span>3.</span>\\n<p>Linear (in_features=200, out_features=2, bias=True)))</p>\\n</li>\\n</ol><p>It should be:</p><p>MLP ((model): Sequential ((0): Linear (in_features=3, out_features=200, bias=True)</p><ol>\\n<li>\\n<span>1.</span>\\n<p>Dropout (p=0.2, inplace=False)</p>\\n</li>\\n<li>\\n<span>2.</span>\\n<p>ReLU ()</p>\\n</li>\\n<li>\\n<span>3.</span>\\n<p>Linear (in_features=200, out_features=2, bias=True)))</p>\\n</li>\\n</ol><p>The improved MLP comprises linear layers, Dropout, and the ReLU activation function. This architecture is established using the Sequential class to construct a feedforward neural network for sample classification.</p><p>Initially, the linear layer conducts linear transformations to augment the feature information of the samples, with an input dimension of 2 and an output dimension of 200. Dropout is then implemented with a probability of 0.2 for random Dropout, mitigating overfitting. Subsequently, the ReLU non-linear activation function is employed to enhance the network?s non-linear expressive capability. Finally, the linear layer is utilized for dimension reduction and classification purposes.</p><p>The original article was updated.</p><ol data-track-component=\\\"outbound reference\\\"><li data-counter=\\\"1.\\\"><p>Yang et al (2024) FLM-ICR: a federated learning model for classification of internet of vehicle terminals using connection records. 13:57 https://doi.org/10.1186/s13677-024-00623-x</p></li></ol><p>Download references<svg aria-hidden=\\\"true\\\" focusable=\\\"false\\\" height=\\\"16\\\" role=\\\"img\\\" width=\\\"16\\\"><use xlink:href=\\\"#icon-eds-i-download-medium\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\"></use></svg></p><h3>Authors and Affiliations</h3><ol><li><p>School of Computer Science, Xijing University, 710123, Xi’an, China</p><p>Kai Yang, Jiawei Du &amp; Jingchao Liu</p></li><li><p>Guangzhou Institute of Technology, Xidian University, 510555, Guangzhou, China</p><p>Feng Xu</p></li><li><p>Beijing Special Electromechanical Research Institute, 100020, Beijing, China</p><p>Ye Tang</p></li><li><p>The 7th Research Institute of China Electronics Technology Group Corporation, 510310, Guangzhou, China</p><p>Ming Liu &amp; Zhibin Li</p></li></ol><span>Authors</span><ol><li><span>Kai Yang</span>View author publications<p>You can also search for this author in <span>PubMed<span> </span>Google Scholar</span></p></li><li><span>Jiawei Du</span>View author publications<p>You can also search for this author in <span>PubMed<span> </span>Google Scholar</span></p></li><li><span>Jingchao Liu</span>View author publications<p>You can also search for this author in <span>PubMed<span> </span>Google Scholar</span></p></li><li><span>Feng Xu</span>View author publications<p>You can also search for this author in <span>PubMed<span> </span>Google Scholar</span></p></li><li><span>Ye Tang</span>View author publications<p>You can also search for this author in <span>PubMed<span> </span>Google Scholar</span></p></li><li><span>Ming Liu</span>View author publications<p>You can also search for this author in <span>PubMed<span> </span>Google Scholar</span></p></li><li><span>Zhibin Li</span>View author publications<p>You can also search for this author in <span>PubMed<span> </span>Google Scholar</span></p></li></ol><h3>Corresponding author</h3><p>Correspondence to Jiawei Du.</p><h3>Publisher’s Note</h3><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p><p>The online version of the original article can be found at https://doi.org/10.1186/s13677-024-00623-x</p><p><b>Open Access</b> This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.</p>\\n<p>Reprints and permissions</p><img alt=\\\"Check for updates. Verify currency and authenticity via CrossMark\\\" height=\\\"81\\\" loading=\\\"lazy\\\" src=\\\"data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 23.5-23.5c0-6.23-2.48-12.21-6.88-16.62-4.41-4.4-10.39-6.88-16.62-6.88zm0 41.25c-9.8 0-17.75-7.95-17.75-17.75s7.95-17.75 17.75-17.75 17.75 7.95 17.75 17.75c0 4.71-1.87 9.22-5.2 12.55s-7.84 5.2-12.55 5.2z" fill="#535353"/><path d="m41 36c-5.81 6.23-15.23 7.45-22.43 2.9-7.21-4.55-10.16-13.57-7.03-21.5l-4.92-3.11c-4.95 10.7-1.19 23.42 8.78 29.71 9.97 6.3 23.07 4.22 30.6-4.86z" fill="#9c9c9c"/><path d="m.2 58.45c0-.75.11-1.42.33-2.01s.52-1.09.91-1.5c.38-.41.83-.73 1.34-.94.51-.22 1.06-.32 1.65-.32.56 0 1.06.11 1.51.35.44.23.81.5 1.1.81l-.91 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0-.92-.08-1.33-.25-.41-.16-.77-.4-1.08-.7-.3-.31-.54-.69-.72-1.13-.17-.44-.26-.95-.26-1.52zm4.61-.62c0-.55-.11-.98-.34-1.28-.23-.31-.58-.47-1.06-.47-.41 0-.77.15-1.08.45-.31.29-.5.73-.57 1.3zm3.01 2.23c.31.24.61.43.92.57.3.13.63.2.98.2.38 0 .65-.08.83-.23s.27-.35.27-.6c0-.14-.05-.26-.13-.37-.08-.1-.2-.2-.34-.28-.14-.09-.29-.16-.47-.23l-.53-.22c-.23-.09-.46-.18-.69-.3-.23-.11-.44-.24-.62-.4s-.33-.35-.45-.55c-.12-.21-.18-.46-.18-.75 0-.61.23-1.1.68-1.49.44-.38 1.06-.57 1.83-.57.48 0 .91.08 1.29.25s.71.36.99.57l-.74.98c-.24-.17-.49-.32-.73-.42-.25-.11-.51-.16-.78-.16-.35 0-.6.07-.76.21-.17.15-.25.33-.25.54 0 .14.04.26.12.36s.18.18.31.26c.14.07.29.14.46.21l.54.19c.23.09.47.18.7.29s.44.24.64.4c.19.16.34.35.46.58.11.23.17.5.17.82 0 .3-.06.58-.17.83-.12.26-.29.48-.51.68-.23.19-.51.34-.84.45-.34.11-.72.17-1.15.17-.48 0-.95-.09-1.41-.27-.46-.19-.86-.41-1.2-.68z" fill="#535353"/></g></svg>\\\" width=\\\"57\\\"/><h3>Cite this article</h3><p>Yang, K., Du, J., Liu, J. <i>et al.</i> Correction: FLM-ICR: a federated learning model for classification of internet of vehicle terminals using connection records. <i>J Cloud Comp</i> <b>13</b>, 75 (2024). https://doi.org/10.1186/s13677-024-00638-4</p><p>Download citation<svg aria-hidden=\\\"true\\\" focusable=\\\"false\\\" height=\\\"16\\\" role=\\\"img\\\" width=\\\"16\\\"><use xlink:href=\\\"#icon-eds-i-download-medium\\\" xmlns:xlink=\\\"http://www.w3.org/1999/xlink\\\"></use></svg></p><ul data-test=\\\"publication-history\\\"><li><p>Published<span>: </span><span><time datetime=\\\"2024-03-26\\\">26 March 2024</time></span></p></li><li><p>DOI</abbr><span>: </span><span>https://doi.org/10.1186/s13677-024-00638-4</span></p></li></ul><h3>Share this article</h3><p>Anyone you share the following link with will be able to read this content:</p><button data-track=\\\"click\\\" data-track-action=\\\"get shareable link\\\" data-track-external=\\\"\\\" data-track-label=\\\"button\\\" type=\\\"button\\\">Get shareable link</button><p>Sorry, 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Correction: FLM-ICR: a federated learning model for classification of internet of vehicle terminals using connection records

Correction: Journal of Cloud Computing (2024) 13:57

https://doi.org/10.1186/s13677-024-00623-x

Following publication of the original article [1], we have been notified that there is duplicate of the body text in the published article.

Now the text is:

MLP ((model): Sequential ((0): Linear (in_features=3, out_features=200, bias=True)

  1. 1.

    Dropout (p=0.2, inplace=False)

  2. 2.

    ReLU ()

  3. 3.

    Linear (in_features=200, out_features=2, bias=True)))

The improved MLP comprises linear layers, Dropout, and the ReLU activation function. This architecture is established using the Sequential class to construct a feedforward neural network for sample classification.

Initially, the linear layer conducts linear transformations to augment the feature information of the samples, with an input dimension of 2 and an output dimension of 200. Dropout is then implemented with a probability of 0.2 for random Dropout, mitigating overfitting. Subsequently, the ReLU non-linear activation function is employed to enhance the network?s non-linear expressive capability. Finally, the linear layer is utilized for dimension reduction and classification purposes.

MLP ((model): Sequential ((0): Linear (in_features=3, out_features=200, bias=True)

  1. 1.

    Dropout (p=0.2, inplace=False)

  2. 2.

    ReLU ()

  3. 3.

    Linear (in_features=200, out_features=2, bias=True)))

It should be:

MLP ((model): Sequential ((0): Linear (in_features=3, out_features=200, bias=True)

  1. 1.

    Dropout (p=0.2, inplace=False)

  2. 2.

    ReLU ()

  3. 3.

    Linear (in_features=200, out_features=2, bias=True)))

The improved MLP comprises linear layers, Dropout, and the ReLU activation function. This architecture is established using the Sequential class to construct a feedforward neural network for sample classification.

Initially, the linear layer conducts linear transformations to augment the feature information of the samples, with an input dimension of 2 and an output dimension of 200. Dropout is then implemented with a probability of 0.2 for random Dropout, mitigating overfitting. Subsequently, the ReLU non-linear activation function is employed to enhance the network?s non-linear expressive capability. Finally, the linear layer is utilized for dimension reduction and classification purposes.

The original article was updated.

  1. Yang et al (2024) FLM-ICR: a federated learning model for classification of internet of vehicle terminals using connection records. 13:57 https://doi.org/10.1186/s13677-024-00623-x

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Authors and Affiliations

  1. School of Computer Science, Xijing University, 710123, Xi’an, China

    Kai Yang, Jiawei Du & Jingchao Liu

  2. Guangzhou Institute of Technology, Xidian University, 510555, Guangzhou, China

    Feng Xu

  3. Beijing Special Electromechanical Research Institute, 100020, Beijing, China

    Ye Tang

  4. The 7th Research Institute of China Electronics Technology Group Corporation, 510310, Guangzhou, China

    Ming Liu & Zhibin Li

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  6. Ming LiuView author publications

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Corresponding author

Correspondence to Jiawei Du.

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The online version of the original article can be found at https://doi.org/10.1186/s13677-024-00623-x

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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Yang, K., Du, J., Liu, J. et al. Correction: FLM-ICR: a federated learning model for classification of internet of vehicle terminals using connection records. J Cloud Comp 13, 75 (2024). https://doi.org/10.1186/s13677-024-00638-4

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