Abstract: Federated learning is to solve the problem of data fragmentation and isolation in machine learning based on privacy protection.Each client node trains the data locally and uploads the model parameter information to the central server,which aggregates the parameter information to achieve the purpose of common training.In the real environment,the distribution of data among nodes is often inconsistent.By analyzing the influence of independent identically distributed data on the accuracy of federated learning,it is proved that the accuracy of the model obtained by the traditional federated learning method is low.Therefore,a diversified sampling strategy is adopted to simulate the data inclination distribution,and a Clustered Federated Learning Methods algorithm based on DBSCAN clustering(DCFL) is proposed,which solves the problem that the learning accuracy is reduced when the data of different nodes are not independently and identically distributed in federated learning.Through the experimental comparison of Mnist and Cifar-10 standard data sets,compared with the traditional federated learning algorithm,DCFL can greatly improve the accuracy of the model.

Key words: Client selection, Cluster, Data distribution, Federated learning, Training optimization