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Federated learning is a framework for obtaining machine learning models without centralized data training,reducing the risk of privacy leakage while also obtaining optimized training models locally. However, the identity,behavior, environment, etc. between nodes are different, resulting in unbalanced data distribution, which may cause a large deviation in the performance of the model on different devices, resulting in data heterogeneity. Aiming at the above problems, this paper proposes a Federated learning algorithm for data sharing clustering based on node optimization method that applies clustering and data sharing to federated learning system at the same time, which can effectively reduce the impact of data heterogeneity on federated learning and accelerate the convergence of local models.At the same time, a method to assess the convergence of the global shared model is designed to determine the timing of node clustering nodes. Finally, in this paper, the datasets EMNIST and CIFAR-10 were used for experiments and performance analysis to compare the effects of the size of the shared scale on the convergence speed and accuracy of each node, and to compare the accuracy of clustering and data sharing before and after the application of federated learning. Experimental results show that the convergence speed and accuracy of each node are improved when data sharing is introduced, and the accuracy is increased by about 10-15% when clustering and data sharing are introduced into federated learning training at the same time, indicating that this method has a good effect on the heterogeneous problem of federated learning data. [ABSTRACT FROM AUTHOR]

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