The multi-client brain tumor classification method based on the convolutional block attention module has inadequate extraction of tumor region details from MRI images, and channel attention and spatial attention interfere with each other under the federated learning framework. In addition, the accuracy in classifying medical tumor data from multiple points is low. To address these problems, this study proposes a brain tumor classification method that amalgamates the federated learning framework with an enhanced CBAM-ResNet18 network. The method leverages the federated learning characteristic to collaboratively work with brain tumor data from multiple sources. It replaces the ReLU activation function with Leaky ReLU to mitigate issues of neuron death. The channel attention module within the convolutional block attention module is modified from a dimension reduction followed by a dimension increment approach to a dimension increment followed by a dimension reduction approach. This change significantly enhances the network’s ability to extract image details. Furthermore, the architecture of the channel attention module and spatial attention module in the convolutional block attention module has been shifted from a cascade structure to a parallel structure, ensuring that the network’s feature extraction capability remains unaffected by the order of processing. A publicly available brain tumor MRI dataset from Kaggle is used in the study. The results demonstrate that FL-CBAM-DIPC-ResNet has a remarkable performance. It achieves impressive accuracy, precision, recall, and F1 score of 97.78%, 97.68%, 97.61%, and 97.63%, respectively. These values of accuracy, precision, recall, and F1 score are 6.54%, 4.78%, 6.80%, and 7.00% higher than those of the baseline model. These experimental findings validate that the proposed method not only overcomes data islands and enables data fusion from multiple sources but also outperforms the majority of existing mainstream models in terms of performance.