<p>Accurate traffic flow prediction plays a pivotal role in intelligent transportation systems, and the integration of graph neural networks and federated learning offers a novel technological approach in this domain. However, existing methods that combine the two still face several challenges, including the difficulty of adapting static graphs to dynamic traffic changes, the inability of the region partitioning method to capture fine-grained spatio-temporal dependencies, and excessive communication overhead under the federated learning framework. To address these issues, this paper proposes a distributed traffic flow prediction method based on a Node-level Federated Dynamic Graph Convolutional Network (NFDGCN). NFDGCN decouples spatio-temporal correlation learning based on a node-level federated framework: on the node side, a context-enhanced temporal embedding mechanism is constructed and a self-attention network is employed to learn local temporal correlations; on the server side, a flow feature-driven dynamic graph construction method is proposed, and an improved graph convolutional network is utilized to learn global spatial correlations, ultimately achieving accurate prediction of dynamic traffic flow. Furthermore, a co-optimization scheme combining a Top-K sparse communication mechanism and the FedAvg aggregation algorithm is designed to significantly reduce communication burden between nodes and the server under the federated learning framework. Experimental results on two real-world datasets demonstrate that NFDGCN achieves average reductions of 4.91%, 5.21%, and 5.85% in MAE, MAPE, and RMSE, respectively, compared to the best-performing baseline, while reducing its communication cost by an average of 74.17%.</p>