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， so that it is difficult to meet the traffic prediction requirements of the whole city scale. Therefore， a distributed wireless traffic prediction framework under cloud-edge collaboration was proposed to realize traffic prediction based on single grid base station with low complexity and communication overhead. Based on the distributed architecture， a wireless traffic prediction model based on federated learning was proposed. Each grid traffic prediction model was trained synchronously， JS （Jensen-Shannon） divergence was used to select grid traffic models with similar traffic distributions through the center cloud server， and Federated Averaging （FedAvg） algorithm was used to fuse the parameters of the grid traffic models with similar traffic distributions， so as to improve the model generalization and describe the regional traffic accurately at the same time. In addition， as the traffic in different areas within the city was highly differentiated in features， on the basis of the algorithm， a federated training method based on coalitional game was proposed. Combined with super-additivity criteria， the grids were taken as participants in the coalitional game， and screened. And the core of the coalitional game and the Shapley value were introduced for profit distribution to ensure the stability of the alliance， thereby improving the accuracy of model prediction. Experimental results show that taking Short Message Service （SMS） traffic as an example， compared with grid-independent training， the proposed model has the prediction error decreased most significantly in the suburb， with a decline range of 26.1% to 28.7%， the decline range is 0.7% to 3.4% in the urban area， and 0.8% to 4.7% in the downtown area. Compared with the grid-centralized training， the proposed model has the prediction error in the three regions decreased by 49.8% to 79.1%.