  Knowledge graph completion (KGC) aims to predict missing facts in knowledge
graphs (KGs), which is crucial as modern KGs remain largely incomplete. While
training KGC models on multiple aligned KGs can improve performance, previous
methods that rely on transferring raw data among KGs raise privacy concerns. To
address this challenge, we propose a new federated learning framework that
implicitly aggregates knowledge from multiple KGs without demanding raw data
exchange and entity alignment. We treat each KG as a client that trains a local
language model through textbased knowledge representation learning. A central
server then aggregates the model weights from clients. As natural language
provides a universal representation, the same knowledge thus has similar
semantic representations across KGs. As such, the aggregated language model can
leverage complementary knowledge from multilingual KGs without demanding raw
user data sharing. Extensive experiments on a benchmark dataset demonstrate
that our method substantially improves KGC on multilingual KGs, achieving
comparable performance to state-of-the-art alignment-based models without
requiring any labeled alignments or raw user data sharing. Our codes will be
publicly available.
