  Federated Knowledge Graph Embedding (FKGE) has recently garnered considerable
interest due to its capacity to extract expressive representations from
distributed knowledge graphs, while concurrently safeguarding the privacy of
individual clients. Existing FKGE methods typically harness the arithmetic mean
of entity embeddings from all clients as the global supplementary knowledge,
and learn a replica of global consensus entities embeddings for each client.
However, these methods usually neglect the inherent semantic disparities among
distinct clients. This oversight not only results in the globally shared
complementary knowledge being inundated with too much noise when tailored to a
specific client, but also instigates a discrepancy between local and global
optimization objectives. Consequently, the quality of the learned embeddings is
compromised. To address this, we propose Personalized Federated knowledge graph
Embedding with client-wise relation Graph (PFedEG), a novel approach that
employs a client-wise relation graph to learn personalized embeddings by
discerning the semantic relevance of embeddings from other clients.
Specifically, PFedEG learns personalized supplementary knowledge for each
client by amalgamating entity embedding from its neighboring clients based on
their "affinity" on the client-wise relation graph. Each client then conducts
personalized embedding learning based on its local triples and personalized
supplementary knowledge. We conduct extensive experiments on four benchmark
datasets to evaluate our method against state-of-the-art models and results
demonstrate the superiority of our method.
