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. Dropout (p=0.2, inplace=False)

• 2. ReLU ()

• 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. Dropout (p=0.2, inplace=False)

• 2. ReLU ()

• 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. Dropout (p=0.2, inplace=False)

• 2. ReLU ()

• 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.