# Class Imbalanced Medical Image Classification Based on Semi-Supervised Federated Learning
###### Abstract
This approach largely alleviates the problem of data-level imbalance, but ignores the value of labeled data and requires training in large batches to optimize the class imbalance problem, otherwise the client experiences severe model degradation in some places, which leads to serious degradation in the performance of the aggregated model, as also confirmed in the experimental Section 3.2. Meanwhile, in FedAvg [22], FedIRM [12], FedPerl [21], and imFed-Semi [18] studies, which have federated learning as a framework, it is common to divide the ratio of labeled to unlabeled data in the experimental data into 2.8, where the labeled data are divided into a small number (1-4) of labeled local clients. The unlabeled data are divided into a majority of unlabeled local clients, and the studies do not investigate the effect of a different number of local clients on model classification. In addition, the performance of the model is only measured using various metrics in the model validation phase, and there is no research on how to represent the robustness of the model. Based on the above problems, how to effectively extract valuable information from labeled data and better overcome the class imbalance problem has become a key issue. The thought is to apply and process the labeled data several times so that it can play an auxiliary role in the process of client-side training. Based on the similarity index of disease presentation [23], it was indicated that the distribution of diseases between different categories of the same type still presents a high degree of similarity across geographic areas. With this discovery, we consider that the validity of training in each client can be guaranteed with a reasonable distribution of data, and this makes it possible to supervise the pseudo-labeling of its predictions. In addition, we think it is also significant to choose what kind of unlabeled data should participate in the training, because the data selection can mitigate the task loss of unlabeled clients and effectively utilize the unlabeled samples. In our article, we present a semi-supervised federated learning approach based on a combination of regularization and pseudo-label construction. We labeled data for model pre-training, local client fusion training, and constraining the generation of pseudo-labels inside each client. Additionally, to better shape the decision boundaries for fewer categories in unlabeled data, we considered using a data selector to filter partially stable unlabeled data in combination with the existing SSL algorithm [24]. To validate the performance of the model, we present a method to measure the robustness of the model. We experimentally validated it on the ISIC 2018: skin lesion analysis for melanoma detection data set [25] with the class sample distribution of the training and test sets as shown in Figure 1. Our method improves all metrics compared to other advanced federated learning methods [12; 18; 22] for the same data sample, and the model has optimal robustness. The main contributions of our article are as follows:  
* To the extent of our knowledge, we present the first approach that combines regularization constraints with pseudo-label construction in solving a federated learning for medical information classification tasks. * We propose a stable selector to filter unlabeled data to improve the robustness of the model and the pseudo-labeling information. * We construct controllable data samplers that can divide labeled and unlabeled data in arbitrary proportions to explore the impact of different numbers of clients on the model. Figure 1: (**a**) The distribution of samples with labels in the central server is shown in red, and the distribution of samples without labels in the local client is shown in green. (**b**) Sample distribution of test set categories for model detection. * We suggest a measure of model robustness that measures the sensitivity of the model to the class of data.