  Federated learning (FL) can be essential in knowledge representation,
reasoning, and data mining applications over multi-source knowledge graphs
(KGs). A recent study FedE first proposes an FL framework that shares entity
embeddings of KGs across all clients. However, entity embedding sharing from
FedE would incur a severe privacy leakage. Specifically, the known entity
embedding can be used to infer whether a specific relation between two entities
exists in a private client. In this paper, we introduce a novel attack method
that aims to recover the original data based on the embedding information,
which is further used to evaluate the vulnerabilities of FedE. Furthermore, we
propose a Federated learning paradigm with privacy-preserving Relation
embedding aggregation (FedR) to tackle the privacy issue in FedE. Besides,
relation embedding sharing can significantly reduce the communication cost due
to its smaller size of queries. We conduct extensive experiments to evaluate
FedR with five different KG embedding models and three datasets. Compared to
FedE, FedR achieves similar utility and significant improvements regarding
privacy-preserving effect and communication efficiency on the link prediction
task.
