# Class Imbalanced Medical Image Classification Based on Semi-Supervised Federated Learning
## 3 Results
### Internal Comparative Analysis of Methods  
Dynamic learning within categories: In Figure 5(a), the sensitivity change curves of each category under our method, and in Figure 5(b) the sensitivity change curves of each category under FedAvg [22] model are shown and set as the baseline level for comparison with our method. The horizontal axis shows 30 class sampling points sampled in 10 cycles, and the vertical axis indicates the change in sensitivity of each class in the test set as the iteration cycle progresses. From the observation we can infer that with the convergence of training almost all sensitivities of all classes are higher than the baseline level. Our method can learn \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{8}{c}{**Category**} \\  & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **Average** \\ \hline FedAvg & 44.40 & 92.99 & 69.63 & 42.05 & 57.83 & 60.83 & 70.00 & 62.53 \\ FedIRM(10) & 61.23 & 88.73 & 0 & 0 & 60.59 & 0 & 0 & 30.07 \\ FedIRM(best) & 58.15 & 80.76 & 87.96 & 45.59 & 66.01 & 33.33 & 11.75 & 54.79 \\ imFed-Semi(10) & 29.07 & 99.40 & 74.07 & 50.00 & 39.40 & 16.66 & 78.12 & 55.24 \\ imFed-Semi(best) & 52.86 & 99.11 & 77.78 & 57.35 & 49.75 & 87.50 & 78.13 & 71.78 \\ our & 53.30 & 96.34 & 63.88 & 60.29 & 63.05 & 87.50 & 87.50 & 73.12 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of the sensitivity of each category under different methods. knowledge effectively from unlabeled data and improve the network performance, to not only have higher recognition for multi-category data, but also have a greater improvement in the recognition rate for fewer category data. Figure 7 reflects the changes of each loss for the training set and the test set. It can be observed that at the beginning of the 170 th cycle, each loss curve shows an obvious decreasing trend, among which the change of auxiliary loss is the most obvious and runs through the other curves. This is, because with the increase in training cycles, the class balance aid gradually produces a constraining effect on the pseudo-label of unlabeled data, and each loss curve has the same change trend among them. This indicates that there is no overfitting phenomenon in the training of the model. This result also demonstrates the effectiveness of our approach of combining consistency constraints with pseudo-labeling in the federated learning for the class imbalance task. Performance Analysis of Fusion Methods: As shown in Table 2, for the comparison of the category analysis performed using the FedAvg [22], FedIRM [12], imFed-Semi [18] method, and our method under different batches, first we can observe that FedAvg [22] can guarantee a higher accuracy rate under the same small batch of training with fewer category samples, which indicates that the category is more distinguishable compared to other categories and reduces the impact caused by fewer data samples. FedIRM [12] shows severe model degradation, with smaller batches of training affecting the supervision of inter-client relationships, as can be observed in comparison to the control in Figure 1b, where, although most categories show a significant improvement in accuracy, a smaller number of four categories show the disappearance of decision boundaries. imFed-Semi [18] shows  
Figure 6: (a) A graph of the sensitivity changes of each category under our method. (b) A graph of the sensitivity changes of each category under the basic network. Figure 7: Variation of loss curve between training set and test set. a significant improvement in the average category sensitivity compared to FedIRM [12] and a significant improvement in the impact caused by data class imbalance. However, due to the uneven distribution of sub-bases during data redistribution and the fact that this phenomenon is constant, which leads to a new imbalance problem in small batches of training, we can find a significant decrease in the accuracy of the first category compared to the other methods.