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.

翻译：联邦知识图谱嵌入（FKGE）因其能够从分布式知识图谱中提取富有表现力的表示，同时保护各客户端的隐私，近来引起了广泛关注。现有的FKGE方法通常利用所有客户端实体嵌入的算术平均值作为全局补充知识，并为每个客户端学习一份全局共识实体嵌入的副本。然而，这些方法通常忽视了不同客户端之间固有的语义差异。这种忽视不仅导致全局共享的补充知识在针对特定客户端定制时被过多噪声淹没，还引发了局部与全局优化目标之间的不一致。因此，所学嵌入的质量受到影响。为解决这一问题，我们提出了基于客户端关系图的个性化联邦知识图谱嵌入（PFedEG），这是一种新颖的方法，通过识别来自其他客户端的嵌入的语义相关性，利用客户端关系图学习个性化嵌入。具体而言，PFedEG通过基于客户端关系图上的“亲和度”聚合来自其相邻客户端的实体嵌入，为每个客户端学习个性化的补充知识。随后，每个客户端基于其本地三元组和个性化补充知识进行个性化嵌入学习。我们在四个基准数据集上进行了大量实验，以评估我们的方法相对于最先进模型的性能，结果证明了我们方法的优越性。