Unaligned Federated Knowledge Graph Embedding.

Recently, knowledge graph embedding (KGE) methods under the federated learning paradigm have received much attention. Its privacy-preserving decentralized training method effectively utilizes the knowledge graphs held by different clients. Existing federated KGE frameworks collaboratively train the global model by aggregating aligned entity embeddings among clients. However, in real-world scenarios, the lack of aligned entities and the high heterogeneity among knowledge graphs constrain their potential. To address these issues, we propose a federated KGE framework that does not depend on any aligned set but uses structure information. The framework introduces a set of basis edges to model the general structure information. Then, we use two separate modules on clients to encode structure and feature representations, respectively. Finally, clients only upload structure parameters for aggregation on the server. The framework uses a new unaligned federated KGE paradigm to tackle the heterogeneity of multi-source knowledge graphs. Experimental results on benchmark datasets show that UniFE achieves superior results even compared to federated KGE frameworks using the aligned set.