Aiming at the problems of federated spatial data isolation， spatial data indexing， and privacy of publishing spatial data， a Federated Spatial data Publishing （FSP） method based on dynamic quad-tree was proposed. Firstly， in each iteration of the FSP method， quad-tree replica was shared by the server with each client in the round， and each client encoded its own location data using the quad-tree replica， and discrete noise was generated through Polya distribution for locally perturbing the encoding results. Secondly， local masks were generated through LWE （Learning With Error） to encrypt the noisy results. Thirdly， the reported values from each client in the iteration were combined by the aggregator to perform secure aggregation and mask elimination. Then the aggregated results were sent to the server. The quad-tree structure was pruned by the server dynamically in a bottom-up way based on the collected encoding vectors and noise variance. Experimental results on four spatial datasets Beijing， Checkin， NYC， and Landmark show that the FSP method not only ensures client privacy， but also reduces the Mean Squared Error （MSE） in federated spatial data publication by 3.80%， 2.96%， 7.51% and 14.13% at a privacy budget of 1.8， respectively， compared to the existing better federated spatial data publication method AHH （Adaptive Hierarchical Histograms）. This indicates that the FSP method achieves higher precision than similar methods in federated spatial data publishing.