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.

翻译：知识图谱由三元组构成，本质上是不完整的，通常需要补全过程来预测缺失的三元组。在现实场景中，知识图谱分布在客户端中，由于隐私限制，补全任务变得更加复杂。现有框架（包括FedE、FedR和FEKG）存在一定局限性：FedE存在信息泄露风险，FedR在关系重叠较少时优化效果降低，FKGE则面临计算成本高和模式崩溃问题。为解决这些问题，我们提出了一种新方法——联邦潜在嵌入共享张量分解（FLEST），这是一种采用联邦张量分解进行知识图谱补全的创新方法。FLEST分解嵌入矩阵，并通过共享潜在字典嵌入来降低隐私风险。实验结果表明，FLEST在性能和隐私之间实现了平衡，既有效又高效。FLEST拓展了联邦张量分解在知识图谱补全任务中的应用。