Abstract: Wireless communication network traffic prediction is of great significance to operators in network construction, base station wireless resource management and user experience improvement. However, the existing centralized algorithm models face the problems of complexity and timeliness, thus difficult to meet the traffic prediction of the whole city scale. A distributed urban global traffic prediction algorithm Fed-DenseNet is proposed in this paper. Each edge computing server of the algorithm performs collaborative training under the coordination of the central server, and the central server uses KL (Kullback-Leibler) divergence to select regional traffic models with similar traffic distribution and uses the federated average algorithm to fuse the parameters of these regional traffic models. In this way, the urban global traffic prediction can be realized with lower complexity and communication cost. In addition, the traffic in different areas within the city is highly differentiated, so how to improve the accuracy of model prediction is also facing challenges. Based on Fed-Densenet algorithm, a personalized federated learning algorithm p-Fed-DenseNet based on cooperative game is proposed. Each regional data feature in the region is taken as a participant of cooperative game, and local features are screened by the super-additivity criterion of cooperative game, so as to achieve the purpose of both improving the generalization of the model and maintaining the accurate description of local traffic.