  With its capability to deal with graph data, which is widely found in
practical applications, graph neural networks (GNNs) have attracted significant
research attention in recent years. As societies become increasingly concerned
with the need for data privacy protection, GNNs face the need to adapt to this
new normal. Besides, as clients in Federated Learning (FL) may have
relationships, more powerful tools are required to utilize such implicit
information to boost performance. This has led to the rapid development of the
emerging research field of federated graph neural networks (FedGNNs). This
promising interdisciplinary field is highly challenging for interested
researchers to grasp. The lack of an insightful survey on this topic further
exacerbates the entry difficulty. In this paper, we bridge this gap by offering
a comprehensive survey of this emerging field. We propose a 2-dimensional
taxonomy of the FedGNNs literature: 1) the main taxonomy provides a clear
perspective on the integration of GNNs and FL by analyzing how GNNs enhance FL
training as well as how FL assists GNNs training, and 2) the auxiliary taxonomy
provides a view on how FedGNNs deal with heterogeneity across FL clients.
Through discussions of key ideas, challenges, and limitations of existing
works, we envision future research directions that can help build more robust,
explainable, efficient, fair, inductive, and comprehensive FedGNNs.
