# Federated Pseudo-Sample Clustering Algorithm:
###### Abstract  
In the realm of personalized federated learning, some current methods substitute shared parameters with shared samples created by Generative Adversarial Networks (GANs). This enables each client to independently design the architecture of their neural network model. However, this approach still fails to overcome the restriction of enforcing uniform labels on client models that partake in training. To tackle this problem, we suggest the Federated Pseudo-Sample Clustering Algorithm (LPFL-GD). This method allows clients to train cooperatively under personalized labeling conditions. The approach uses the local model as a discriminator and forms a GAN network with the generator to produce a sample set. This set is then uploaded to the central server. The uploaded shared samples are clustered and divided into several clusters by introducing the DBSCAN algorithm on the central server. When filtering client samples, we obtain the labels of shared samples from each client in a cluster and correct the label of the entire cluster. We then merge the corrected samples with the local dataset to extend it. Our approach improves model performance, even when different clients label the same type of data differently. Compared with their performance before participating in federated learning, our approach can improve client model accuracy by up to 13.4%. We replicated other methods in the same environment and found that the local model accuracy of those methods not only improved very little but even decreased by up to 34.5%. label-personalized federated learning; generative adversarial networks; DBSCAN algorithm +
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their specific task requirements, resulting in inherent diversity. Therefore, [PERSON] et al. have proposed a personalized federated learning technique that utilizes generative adversarial networks. This method caters to differences in model structures among clients. However, it still requires participating clients to standardize the number and sequence of labels during training. This not only reveals the local label information of clients but also makes it impossible for clients with different classification tasks but similar training data to participate in the training. Although the tasks differ, the data types across clients are similar. For example, the MNIST dataset can be used for both binary classification tasks, such as distinguishing odd and even numbers, and decimal classification tasks, such as recognizing individual numbers. By enabling clients with different tasks but similar training data to participate in federated learning simultaneously, we can expand the range of training data and enhance the privacy and security of clients. To address the above challenges, we propose a personalized federated learning method for labels based on image clustering. In this approach, shared samples are clustered and filtered, and sample labels are corrected. The corrected samples are then combined with local data to create a new dataset for subsequent training, thereby facilitating collaborative training between clients for label personalization. Our research makes the following key contributions:  
1. We pose a label-personalized federated learning problem. This means that due to different classification tasks, different clients can label the same type of data with different labels. 2. We propose a label-personalized federated learning method, LPFL-GD, which enables collaborative training between clients under the label personalization condition by introducing image clustering. 3. We use the image clustering method to solve the problem that the samples generated by the shared generation adversarial network cannot realize federated learning under the condition of label personalization. 4. We analyze the convergence of the method theoretically and provide proof of convergence. The remainder of this paper is organized as follows. Section 2 reviews related research. Section 3 formulates the label personalization problem. Section 4 describes the proposed LPFL-GD method in detail.