Federated learning is widely used and researched as an effective method for solving the privacy problems faced by centralized learning. To address the communication limitations and heterogeneity among clients, many existing methods based on the mixup algorithm share data mixed with the client’s local dataset to improve the model accuracy. However, due to the heterogeneity of federated learning, there may be some clients who join the mixup process with insufficient data, which will violate the privacy-preserving assumption of the mixup. Because of this weakness, many methods based on the mixup approach will face a serious privacy problem while trying to improve federated learning over other parts, e.g., accuracy or communication efficiency. Therefore, we propose the FedMDO framework to solve the privacy problem faced by mixup-based methods. In FedMDO, we introduce the auxiliary client to hold the auxiliary dataset that is related to the federated learning task and to generate the mixup templates for clients to increase the amount of data in the mixup process. By introducing the auxiliary client, the decrease in model accuracy can be suppressed as much as possible while taking advantage of the privacy-preserving gain from the increase in data volume. Furthermore, we introduce differential privacy into FedMDO with an elaborate redesign to enhance privacy protection. The corresponding analysis shows that under FedMDO, differential privacy can achieve the same protection with less negative impact. Experiments show that with at least an approximately 10%+ improvement in model accuracy and an average of 5 times greater communication savings compared to the FedAvg and non-IID SOTAs with weak privacy protection design, our method can yield significant improvement in the privacy of the shared data.