GFedKG: GNN-based federated embedding model for knowledge graph completion

Knowledge Graph (KG) completion through reasoning over KGs is one of the most concerned issue in research around knowledge graphs. Current work focuses more on studying individual KG, while a series of related multi-source KGs are more common in real applications. In order to effectively exert their complementary roles in completing each KG under the premise of ensuring data privacy, federated learning becomes the focus of attention. However, existing methods ignore the explicit complementarity among KGs in terms of structural information. And they generally align embeddings of overlapping instances across clients, which constrains flexibility of each client model. Based on this, we propose the first GNN-based federated embedding framework for multiple knowledge graph completion, GFedKG. It focuses on the complementary role among KGs in topology structures and adaptively generates different aggregated neighboring features for entities in distinct clients. Such treatment achieves some consistency among clients while retaining the flexibility of each client model to characterize respective KG. Moreover, entity embeddings that represent the features of entities are better privacy-preserved since neighboring embeddings are transmitted instead of them. And with the aim of realizing more refined and accurate aggregation of neighboring features from multiple clients, we innovatively propose a context-aware graph neural network, along with the context-enhanced evaluation function. Homophily neighbors are also taken into account to cope with the low-homophily aspect of KGs. Extensive experiments demonstrate the advantages of GFedKG.