# Federated Pseudo-Sample Clustering Algorithm:
## 5 Experiments
### Compare with Other Federated Learning Methods  
This paper compares the experimental results with existing federated learning methods, including PerFED-GAN [16], RHFL [42], and Fedmd [43]. The main work in this paper is the clustering of shared samples by the central server. The clustered shared samples are used to filter and correct the sample labels according to the categories of the local client samples in order to avoid the problem of model performance degradation due to different clients labeling the same type of samples with different labels. Therefore, comparing the mean relative test accuracy of the model of the non-clustered PerFED-GAN method with the mean relative test accuracy of the model in our experiments can reflect the importance of our method. The different number of classes in different classification tasks leads to variations in the local model structures. There are relatively few federated learning methods that consider model heterogeneity. In this study, we compare our method with the RHFL and Fedmd methods. Due to the requirement of aligning average scores, the Fedmd method cannot achieve collaborative federated learning with different numbers of classes. In order to demonstrate the effectiveness of our method under the condition of label personalization, we keep the number of classes consistent among all local models but differentiate the label arrangements. As shown in Figures 4 and 5, our proposed method significantly improves the model performance under both IID and non-IID experimental conditions. In contrast, using the PerFED-GAN method leads to a rapid decline in model performance by 14-35% in terms of average accuracy. Figure 3: Results of Fashion-MNIST experiments. (**a**) IID Setting. (**b**) Non-IID Setting. As shown in Figures 6 and 7, the experimental results demonstrate that our proposed method outperforms both the RHFL method and the Fedmd method, regardless of whether under IID or non-IID conditions. During the federated training process, it can even happen that the model accuracy of the RHFL method and Fedmd method is lower than the local model accuracy. In conclusion, based on the experimental results, our method is more suitable for federated learning scenarios with label personalization compared with other methods, and it outperforms the comparison methods in terms of model performance. Particularly, the performance improvement is more significant in non-IID data settings. Analysis of the experimental results for Figures 4 and 5: The classifier is a part of the generative adversarial network. Generative adversarial networks are adversarial training, and in this experiment, the evaluation criterion used is the mean relative test accuracy (23), which is the average of the relative test accuracies of all clients. Therefore, the curve of mean relative test accuracy fluctuates as the number of federated learning rounds increases. In this experiment, the clients retain the model from each round of federated learning. After  
Figure 4: Comparative experimental results of MNIST dataset. (**a**) IID Setting. (**b**) Non-IID Setting. Figure 5: Comparative experimental results of Fashion-MNIST dataset. (**a**) IID Setting. (**b**) Non-IID Setting. the completion of federated training, the model with the highest performance is selected as the final trained model. Analysis of the PerFED-GAN model's sharp performance degradation as the number of federated learning rounds increases: In a heterogeneous label environment, different clients label the same class of images differently. In the process of federated learning, the central server aggregates the samples uploaded by different clients and then randomly assigns them to the local server. As a result, the labels of the locally obtained images are inconsistent with the labels assigned by the local images, leading to the phenomenon of the performance of the local model being degraded during the training process.