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

Download references

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

Authors

1. Kai YangView author publications

   You can also search for this author in PubMed Google Scholar

2. Jiawei DuView author publications

   You can also search for this author in PubMed Google Scholar

3. Jingchao LiuView author publications

   You can also search for this author in PubMed Google Scholar

4. Feng XuView author publications

   You can also search for this author in PubMed Google Scholar

5. Ye TangView author publications

   You can also search for this author in PubMed Google Scholar

6. Ming LiuView author publications

   You can also search for this author in PubMed Google Scholar

7. Zhibin LiView author publications

   You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jiawei Du.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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/.

Reprints and permissions

Cite this article

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

Download citation

• Published: 26 March 2024

• DOI: https://doi.org/10.1186/s13677-024-00638-4

Share this article

Anyone you share the following link with will be able to read this content:

Get shareable link

Sorry, a shareable link is not currently available for this article.

Copy to clipboard

Provided by the Springer Nature SharedIt content-sharing initiative