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Knowledge graphs (KGs), which consist of triples, are inherently incompleteand always require completion procedure to predict missing triples. Inreal-world scenarios, KGs are distributed across clients, complicatingcompletion tasks due to privacy restrictions. Many frameworks have beenproposed to address the issue of federated knowledge graph completion. However,the existing frameworks, including FedE, FedR, and FEKG, have certainlimitations. = FedE poses a risk of information leakage, FedR's optimizationefficacy diminishes when there is minimal overlap among relations, and FKGEsuffers from computational costs and mode collapse issues. To address theseissues, we propose a novel method, i.e., Federated Latent Embedding SharingTensor factorization (FLEST), which is a novel approach using federated tensorfactorization for KG completion. FLEST decompose the embedding matrix andenables sharing of latent dictionary embeddings to lower privacy risks.Empirical results demonstrate FLEST's effectiveness and efficiency, offering abalanced solution between performance and privacy. FLEST expands theapplication of federated tensor factorization in KG completion tasks.

Federated Knowledge Graph Completion via Latent Embedding Sharing and Tensor Factorization