Is Averaging Always the Best? Improving Aggregation Method for Federated Knowledge Graph Embedding

In recent years, along with the rapid development of big data and AI technologies, knowledge graphs have also experienced significant growth. The vectorized representation of entities and relations in knowledge graphs has proven beneficial for various knowledge graph-related applications. However, traditional knowledge graph embedding methods are designed for centralized graphs, which cannot effectively represent the distributed knowledge graphs in real-world while ensuring data security. In this paper, we improve the aggregation method for federated knowledge graph embedding and propose a Federated knowledge graph embedding model with CompGCN, which is called FedComp for short. FedComp is an innovative server-client framework which implement CompGCN for federated KGE, along with the design of three novel aggregation methods. We conduct link prediction experiments on two datasets to demonstrate the performance of our model.