  We address the challenge of federated learning on graph-structured data
distributed across multiple clients. Specifically, we focus on the prevalent
scenario of interconnected subgraphs, where interconnections between different
clients play a critical role. We present a novel framework for this scenario,
named FedStruct, that harnesses deep structural dependencies. To uphold
privacy, unlike existing methods, FedStruct eliminates the necessity of sharing
or generating sensitive node features or embeddings among clients. Instead, it
leverages explicit global graph structure information to capture inter-node
dependencies. We validate the effectiveness of FedStruct through experimental
results conducted on six datasets for semi-supervised node classification,
showcasing performance close to the centralized approach across various
scenarios, including different data partitioning methods, varying levels of
label availability, and number of clients.
