Most existing GNN-based recommendation methods focus on exploiting a user–item heterogeneous graph, which, however, will cause efficiency and effectiveness challenges, in a federated learning setting considering user privacy. We find that a user–user or item–item homogeneous graph is often privacy-insensitive and can significantly enhance the efficiency and effectiveness of federated graph embedding learning. Hence, we propose a novel framework called Federated Homogeneous Graph Neural Network (FedHoG) , which can provide privacy-preserving recommendations with high-quality and communication-efficient graph learning. We first design a privacy-preserving homogeneous graph construction method, which enables the server to construct an item–item graph and a user–user graph without leaking user privacy. Then, we develop a federated homogeneous graph learning method that enables balanced GNN model training among the server and clients. We also propose a lightweight homogeneous graph convolution method to achieve better graph embedding learning. Finally, extensive experiments on three public datasets show the advantages of our FedHoG in performance and efficiency. The datasets, source codes, and scripts are available at https://github.com/XZHhong/FedHoG .