<ul><li>Federated training methods have gained popularity for graph learning with applications including friendship graphs of social media sites and customer-merchant interaction graphs of huge online marketplaces.</li><li>The graph is partitioned across clients due to privacy regulations, preventing clients from accessing information stored on other clients.</li><li>Cross-client edges in the graph present a challenge to federated training methods as training a graph model at one client requires feature information of nodes on the other end of cross-client edges.</li><li>The Federated Graph Attention Network (FedGAT) algorithm is introduced to approximate the behavior of Graph Attention Networks (GATs) for semi-supervised node classification with reduced communication overhead.</li></ul>

FedGAT: A Privacy-Preserving Federated Approximation Algorithm for Graph Attention Networks

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