# Federated Pseudo-Sample Clustering Algorithm:
## 6 Conclusions  
In this paper, we propose the Federated Pseudo-Sample Clustering Algorithm (LPFL-GD). Based on the samples generated by the shared generative adversarial network, the DBSCAN algorithm is introduced on the central server to divide the shared samples into clusters of different categories through image clustering, and to adjust the labels in the clusters according to different clients, in order to adapt to the heterogeneous labeling environment. Since the clustering object is the sample generated by the generative adversarial network, rather than the original data, the parameters of the existing DBSCAN algorithm cannot be directly applied to our experiment. In order to obtain better experimental results, we adjusted the parameters of the DBSCAN algorithm and explained our parameter-tuning method. LPFL-GD can further improve the performance of local models on local datasets by up to 13.4%. The experimental results show that, on the one hand, the adjustment of clustering parameters is more suitable for our experiment than the existing methods, and good results are obtained; on the other hand, compared with the existing federated learning methods, LPFL-GD is more suitable for personalized federated learning scenarios under label-personalized conditions. When clients of different classification criteria cooperate with each other, our proposed method can improve the performance of the model. This extends the scope of federated learning scenarios beyond a single classification standard that all clients must adhere to.  
Limitations of the method proposed in this paper are as follows: 1. Samples generated by GAN networks will reflect the characteristics of the original data, and shared samples may cause local data information leakage to a certain extent, resulting in relatively poor  
Figure 8: Comparative experimental results of the clustering algorithm. (**a**) MNIST. (**b**) Fashion-MNIST.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & **Eps = 0.05** & **Eps = 0.1** & **Eps = 0.15** & **Eps = 0.2** & **Eps = 0.25** & **Eps = 0.3** \\ \hline MINPTS = 5 & 13 & 11 & 6 & 3 & 2 & 1 \\ MINPTS = 10 & 7 & 8 & 6 & 4 & 2 & 1 \\ MINPTS = 15 & 5 & 7 & 7 & 5 & 3 & 1 \\ \hline \end{tabular}
\end{table}
Table 3: The number of cluster categories obtained by different parameters.  
data security performance. 2. The process of sharing and generating the samples generated by the antagonistic network is a heavy burden that provides relatively poor communication efficiency for communication.  
Conceptualization, S.S.; methodology, S.S. and X.L.; software, S.S.; validation, S.S.; data curation, S.S.; writing--original draft preparation, S.S.; supervision, X.L. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the Natural Science Foundation of Hebei Province of China (F2018201197).  
Not applicable.  
Both datasets used in this experiment are publicly available datasets at the following URLs: [[http://yann.lecun.com/exdb/mnist/](http://yann.lecun.com/exdb/mnist/)]([http://yann.lecun.com/exdb/mnist/](http://yann.lecun.com/exdb/mnist/)) (accessed on 1 October 2022), [[https://github.com/zalandoresearch/fashion-mnist](https://github.com/zalandoresearch/fashion-mnist)]([https://github.com/zalandoresearch/fashion-mnist](https://github.com/zalandoresearch/fashion-mnist)) (accessed on 1 October 2022).  
The authors declare that this study was conducted without any commercial or financial relationships that could be considered potential conflicts of interest.