The current assisted pneumonia diagnosis method using the residual network (ResNet) based on batch normalization has high dependence on the batch size and a low utilization rate of network channel features, and pneumonia diagnosis methods using deep neural networks all ignore the problems of medical data privacy and islands. To solve these problems, this study proposes an assisted diagnosis method that integrates the federated learning framework, the squeeze-and-excitation network, and the improved ResNet (FL-SE-ResNet-GN). This method uses FL to protect data privacy and pays full attention to channel characteristics with the SE network and the group normalization method. Experimental results on the Chest X-Ray Images dataset show that the accuracy, precision, and recall of this method reach 0.952, 0.933, and 0.974, respectively. Compared with other existing methods, this method has significantly improved the accuracy and recall indicators on the basis of protecting data privacy.