  Graph neural networks (GNN) have been successful in many fields, and derived
various researches and applications in real industries. However, in some
privacy sensitive scenarios (like finance, healthcare), training a GNN model
centrally faces challenges due to the distributed data silos. Federated
learning (FL) is a an emerging technique that can collaboratively train a
shared model while keeping the data decentralized, which is a rational solution
for distributed GNN training. We term it as federated graph learning (FGL).
Although FGL has received increasing attention recently, the definition and
challenges of FGL is still up in the air. In this position paper, we present a
categorization to clarify it. Considering how graph data are distributed among
clients, we propose four types of FGL: inter-graph FL, intra-graph FL and
graph-structured FL, where intra-graph is further divided into horizontal and
vertical FGL. For each type of FGL, we make a detailed discussion about the
formulation and applications, and propose some potential challenges.
