CE-FedGNN enables federated GNN training on coupled distributed graphs via infrequent aggregated representation exchange, moving-average estimation for staleness, and metric-DP, with O(1/sqrt(T)) convergence and O(T^{3/4}) communication.
Node-level differentially private graph neural networks
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Upper bounds on misclassification rate in DP-GCNs are derived as a function of subsampling probability p_s, with feasible ranges of p_s identified to balance privacy and utility.
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Provably Communication-Efficient and Privacy-Preserving Federated Graph Neural Networks
CE-FedGNN enables federated GNN training on coupled distributed graphs via infrequent aggregated representation exchange, moving-average estimation for staleness, and metric-DP, with O(1/sqrt(T)) convergence and O(T^{3/4}) communication.
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Misclassification Rate and Privacy-Utility Trade-offs in Graph Convolutional Networks via Subsampling Stability
Upper bounds on misclassification rate in DP-GCNs are derived as a function of subsampling probability p_s, with feasible ranges of p_s identified to balance privacy and utility.