FedSPM: Routing-Enabled Federated Learning under Dual Heterogeneity via Semiparametric Mixture

Excerpt

arXiv:2607.04085v2 Announce Type: replace Abstract: 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 each client as internally homogeneous, overlooking latent subpopulations within local data. For example, patients with the same diagnosis at one hos