  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.
