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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Corresponding author

Correspondence to Jiawei Du.

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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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• Published: 26 March 2024

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

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