  Knowledge graphs (KGs), which consist of triples, are inherently incomplete
and always require completion procedure to predict missing triples. In
real-world scenarios, KGs are distributed across clients, complicating
completion tasks due to privacy restrictions. Many frameworks have been
proposed to address the issue of federated knowledge graph completion. However,
the existing frameworks, including FedE, FedR, and FEKG, have certain
limitations. = FedE poses a risk of information leakage, FedR's optimization
efficacy diminishes when there is minimal overlap among relations, and FKGE
suffers from computational costs and mode collapse issues. To address these
issues, we propose a novel method, i.e., Federated Latent Embedding Sharing
Tensor factorization (FLEST), which is a novel approach using federated tensor
factorization for KG completion. FLEST decompose the embedding matrix and
enables sharing of latent dictionary embeddings to lower privacy risks.
Empirical results demonstrate FLEST's effectiveness and efficiency, offering a
balanced solution between performance and privacy. FLEST expands the
application of federated tensor factorization in KG completion tasks.
