  Graph machine learning has gained great attention in both academia and
industry recently. Most of the graph machine learning models, such as Graph
Neural Networks (GNNs), are trained over massive graph data. However, in many
real-world scenarios, such as hospitalization prediction in healthcare systems,
the graph data is usually stored at multiple data owners and cannot be directly
accessed by any other parties due to privacy concerns and regulation
restrictions. Federated Graph Machine Learning (FGML) is a promising solution
to tackle this challenge by training graph machine learning models in a
federated manner. In this survey, we conduct a comprehensive review of the
literature in FGML. Specifically, we first provide a new taxonomy to divide the
existing problems in FGML into two settings, namely, FL with structured data
and structured FL. Then, we review the mainstream techniques in each setting
and elaborate on how they address the challenges under FGML. In addition, we
summarize the real-world applications of FGML from different domains and
introduce open graph datasets and platforms adopted in FGML. Finally, we
present several limitations in the existing studies with promising research
directions in this field.
