  Knowledge graphs (KGs) consisting of triples are always incomplete, so it's
important to do Knowledge Graph Completion (KGC) by predicting missing triples.
Multi-Source KG is a common situation in real KG applications which can be
viewed as a set of related individual KGs where different KGs contains
relations of different aspects of entities. It's intuitive that, for each
individual KG, its completion could be greatly contributed by the triples
defined and labeled in other ones. However, because of the data privacy and
sensitivity, a set of relevant knowledge graphs cannot complement each other's
KGC by just collecting data from different knowledge graphs together.
Therefore, in this paper, we introduce federated setting to keep their privacy
without triple transferring between KGs and apply it in embedding knowledge
graph, a typical method which have proven effective for KGC in the past decade.
We propose a Federated Knowledge Graph Embedding framework FedE, focusing on
learning knowledge graph embeddings by aggregating locally-computed updates.
Finally, we conduct extensive experiments on datasets derived from KGE
benchmark datasets and results show the effectiveness of our proposed FedE.
