Federated learning (FL) has attracted increasing attention in recent years. As a privacy-preserving collaborative learning paradigm, it enables a broader range of applications, especially for computer vision and natural language processing tasks. However, to date, there is limited research of federated learning on relational data, namely Knowledge Graph (KG). In this work, we present a modified version of the graph neural network algorithm that performs federated modeling over KGs across different participants. Specifically, to tackle the inherent data heterogeneity issue and inefficiency in algorithm convergence, we propose a novel optimization algorithm, named FedAlign, with 1) optimal transportation (OT) for on-client personalization and 2) weight constraint to speed up the convergence. Extensive experiments have been conducted on several widely used datasets. Empirical results show that our proposed method outperforms the state-of-the-art FL methods, such as FedAVG and FedProx, with better convergence.

本研究提出了一种基于图神经网络的优化算法FedAlign，实现了在知识图谱上的联邦建模，解决了数据异构性和算法收敛效率问题，并在多个常用数据集上进行广泛实验，实验证明该方法比目前最先进的联邦学习方法表现更佳。

通过基准对准和重量惩罚来改进联合关系数据建模