Provably Communication-Efficient and Privacy-Preserving Federated Graph Neural Networks

arXiv:2605.26243v1 Announce Type: new Abstract: Graph neural networks (GNNs) achieve strong performance on relational data, but real-world graphs are often distributed across organizations that cannot share raw data due to privacy and policy constraints. Existing federated GNN methods either ignore cross-client links, leading to degraded accuracy, or require frequent embedding exchanges, incurring substantial communication and privacy costs. We propose CE-FedGNN, a communication-efficient and privacy-preserving federated GNN framework for learning over such coupled graphs. Our approach avoids

Source: arXiv cs.LG — read the full report at the original publisher.

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