  Knowledge graph embedding plays an important role in knowledge
representation, reasoning, and data mining applications. However, for multiple
cross-domain knowledge graphs, state-of-the-art embedding models cannot make
full use of the data from different knowledge domains while preserving the
privacy of exchanged data. In addition, the centralized embedding model may not
scale to the extensive real-world knowledge graphs. Therefore, we propose a
novel decentralized scalable learning framework, \emph{Federated Knowledge
Graphs Embedding} (FKGE), where embeddings from different knowledge graphs can
be learnt in an asynchronous and peer-to-peer manner while being
privacy-preserving. FKGE exploits adversarial generation between pairs of
knowledge graphs to translate identical entities and relations of different
domains into near embedding spaces. In order to protect the privacy of the
training data, FKGE further implements a privacy-preserving neural network
structure to guarantee no raw data leakage. We conduct extensive experiments to
evaluate FKGE on 11 knowledge graphs, demonstrating a significant and
consistent improvement in model quality with at most 17.85\% and 7.90\%
increases in performance on triple classification and link prediction tasks.
