Due to limitations in the performance of client-side systems and constraints on communication costs, most existing Federated Learning (FL) algorithms cannot involve all clients in training. Therefore, randomly selecting some clients to participate in FL training is used in practice. However, the datasets held by clients often exhibit non-independent and identically distributed (Non-IID) characteristics. This method of randomly selecting clients can lead to training the global model on more unbalanced datasets, ultimately decreasing the global model’s performance. To effectively mitigate the impact of dataset imbalance on Federated Learning (FL), in this paper, we propose a class-balanced sampling method based on the grouping of the number of client classes - FedCCBS (Federated Client Class Balanced Sampling). It aims to select clients with complementary datasets for training, thereby alleviating the adverse effects of imbalanced datasets on the global model. We conducted experiments on the MNIST, FASHION MNIST and CIFAR-10 datasets, and the experimental results demonstrated that FedCCBS achieves faster convergence and maintains a more stable convergence process. Moreover, the classification accuracy of FedCCBS surpasses that of other baseline algorithms.