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.

使用客户关系图进行个性化联邦知识图嵌入的方法（PFedEG），通过识别来自其他客户端的嵌入的语义关联性，为每个客户端学习个性化的嵌入，从而解决了现有 FKGE 方法中的全局共享补充知识被噪音淹没和局部与全局优化目标不一致的问题。

个性化联邦知识图嵌入与客户关系图

Personalized Federated Knowledge Graph Embedding with Client-Wise  Relation Graph

Knowledge sharing and model personalization are two key components in the
conceptual framework of personalized federated learning (PFL). Existing PFL
methods focus on proposing new model personalization mechanisms while simply
implementing knowledge sharing by aggregating models from all clients,
regardless of their relation graph. This paper aims to enhance the
knowledge-sharing process in PFL by leveraging the graph-based structural
information among clients. We propose a novel structured federated learning
(SFL) framework to learn both the global and personalized models simultaneously
using client-wise relation graphs and clients' private data. We cast SFL with
graph into a novel optimization problem that can model the client-wise complex
relations and graph-based structural topology by a unified framework. Moreover,
in addition to using an existing relation graph, SFL could be expanded to learn
the hidden relations among clients. Experiments on traffic and image benchmark
datasets can demonstrate the effectiveness of the proposed method. All
implementation codes are available on Github

本文提出了一种基于图结构的结构化联邦学习框架（SFL），通过使用客户端之间的关系图形信息来加强 PFL 中的知识共享过程，并能够同时学习全局和个性化模型。SFL 可扩展到学习客户之间的隐藏关系，并证明了其在交通和图像基准数据集上的有效性。