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FedSPM: Routing-Enabled Federated Learning under Dual Heterogeneity via Semiparametric Mixture
Routing-prediction federated learning has emerged as a new paradigm that reframes inter-client heterogeneity as a resource for system-level intelligence: at inference time, the server routes each external query to the best-matched client for prediction. Existing approaches, however, typically treat
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