FedCKE: Cross-Domain Knowledge Graph Embedding in Federated Learning

Representing the structural relations between entities, i.e., knowledge graph embedding, which is a method to learn low-dimensional representations of knowledge, has become an increasingly prevalent research orientation in cognitive and human intelligence. It is significant to study how to interrelate, fuse and embed the knowledge graph data from different domains while considering data not shared. In this paper, we propose a model of cross-domain knowledge graph embedding in federated learning (FedCKE), in which entity/relation embedding between different domains can interact securely in the case that data is not shared. In advance of client model training, we present an inter-domain encrypted entity/relation alignment method using the encrypted sample alignment method in vertical federated learning, which can obtain entity/relation intersections between different domains without revealing any triples structure and additional entities/relations in the respective datasets. On the server, we aggregate the same entity/relation embeddings by the association in conjunction with the parameter-secure aggregation method in horizontal federated learning. Experimental results on three real datasets show that the proposed FedCKE model is able to enhance the embedding of different clients (domains).