## 3 Results
### Experimental Analysis of Distributed Learning Classification Module
#### 3.4.2 Selection of the Number of Clients  
In order to investigate the impact of the number of clients on classification accuracy, we conducted five control experiments, as described in the following. Use one client, three clients, five clients, ten clients, and twenty clients for local iteration. After each client iteration, upload their respective parameters to the server, which aggregates the global parameters to complete one round of model training. Record the learning results of 30 rounds and draw a comparison chart. From Figure 13, it can be seen that as the number of model training rounds increases, the classification accuracy of the five models gradually improves. After 30 rounds of training, the five models tend to stabilize, and the classification accuracy of the test set (divided into the test set and training set in a 3:7 ratio) exceeds 90%. Among these, 20 client end models have the highest classification accuracy, reaching 99.12%. This indicates that increasing the number of clients can improve the accuracy of model classification.  
Although the accuracy of model classification increases with the number of clients, we ultimately choose 10 clients instead of 20 clients for training. On the one hand, the classification accuracy of 20 clients is not much improved compared to 10 clients. The classification accuracy of 20 clients is 99.12%, while the classification accuracy of 10 clients is 98.96%. Setting up 10 clients can sufficiently meet the classification requirements. On the other hand, increasing the number of clients will consume network IO resources and server performance, so we finally selected 10 clients for training.  
We used the parameters determined by comparative experiments to train the distributed model. After 30 rounds of training, we obtained the classification confusion matrix of the four pathogenic bacteria. As shown in Figure 14, our method achieved good results in this classification task, especially for the classification of _Aeromonas hydroophila_, with an accuracy of 99.7%. However, the classification accuracy of _Aeromonas versonii_ is only 97.9%. We speculate that when obtaining the Raman spectrum data of _Aeromonas versonii_, the spectrometer was interfered with by an external interference, which affected the data. Alternatively, during the microbial culture stage, it may have been affected by other miscellaneous bacteria, resulting in a decrease in the purity of _Aeromonas versonii_.  
Figure 13: The results of different numbers of clients.  
AUC is the area under the ROC curve, which is used to measure classifier performance. The higher the AUC, the higher the model's accuracy. As shown in Figure 15, the average AUC of the classification model for the four pathogenic bacteria is 0.983. For individual pathogenic bacteria, the AUC values are also greater than 0.97, indicating that our proposed classification model is robust.  
Figure 14: Confusion matrix of the test set. The vertical axis represents the true labels, and the horizontal axis represents the predicted labels. The diagonal values are the ratios of correct predictions.