F2STNet combines truncated graph Fourier features, a diagonal state-space temporal layer, and fairness-aware federated aggregation to improve graph forecasting accuracy and client fairness.
FedGCR: Achieving perfor- mance and fairness for federated learning with distinct client types via group customization and reweighting
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F2STNet: Fair and Federated Spectral-Temporal Modeling for Graph Forecasting
F2STNet combines truncated graph Fourier features, a diagonal state-space temporal layer, and fairness-aware federated aggregation to improve graph forecasting accuracy and client fairness.