To address the information silo problem and the risk of location privacy leakage caused by distributed location big data collection， a statistical prediction and privacy protection method for location big data was proposed on the basis of federated learning. Firstly， a horizontal federated learning-based statistical prediction release framework was constructed for location big data. The framework allowed data collectors in each administrative region to keep their raw data， and multiple participants to collaborate to complete the prediction model’s training task by exchanging training parameters. Secondly， PVTv2-CBAM was developed to improve the accuracy of prediction results at clients， aiming for the problem of statistical prediction location big data density with spatiotemporal sequence characteristics. Finally， combined with the MMA （Modified Moments Accountant） mechanism， a dynamic allocation and adjustment algorithm for differential privacy budget was proposed to achieve diffirential privacy protection of the client models. Experimental results show that compared to models such as Convolutional Neural Network （CNN）， Long Short-Term Memory （LSTM）， and Convolutional LSTM （ConvLSTM）the proposed PVTv2-CBAM improves the prediction accuracy by 0 to 62% on the Yellow_tripdata dataset and by 39% to 44% on the T-Driver trajectory dataset；the proposed differential privacy budget dynamic allocation and adjustment algorithm enhances the model prediction accuracy by about 5% and 6% at adjustment thresholds of 0.3 and 0.7， respectively， compared with no dynamic adjustment. The above validates the feasibility and effectiveness of the proposed method.