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Graph machine learning has gained great attention in both academia andindustry recently. Most of the graph machine learning models, such as GraphNeural Networks (GNNs), are trained over massive graph data. However, in manyreal-world scenarios, such as hospitalization prediction in healthcare systems,the graph data is usually stored at multiple data owners and cannot be directlyaccessed by any other parties due to privacy concerns and regulationrestrictions. Federated Graph Machine Learning (FGML) is a promising solutionto tackle this challenge by training graph machine learning models in afederated manner. In this survey, we conduct a comprehensive review of theliterature in FGML. Specifically, we first provide a new taxonomy to divide theexisting problems in FGML into two settings, namely, FL with structured dataand structured FL. Then, we review the mainstream techniques in each settingand elaborate on how they address the challenges under FGML. In addition, wesummarize the real-world applications of FGML from different domains andintroduce open graph datasets and platforms adopted in FGML. Finally, wepresent several limitations in the existing studies with promising researchdirections in this field.

Federated Graph Machine Learning: A Survey of Concepts, Techniques, and Applications