FedGCF fuses clustered structural models and selected node-feature models with a bandit-tuned ratio, claiming accuracy and communication improvements in federated graph classification, though its test-set-based tuning undermines the evaluation.
Federated lea rning-based cross-enterprise recommendation with graph neural networ ks,
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Enhancing Federated Graph Learning via Adaptive Fusion of Structural and Node Characteristics
FedGCF fuses clustered structural models and selected node-feature models with a bandit-tuned ratio, claiming accuracy and communication improvements in federated graph classification, though its test-set-based tuning undermines the evaluation.