The paper derives Wasserstein-based generalization bounds for hierarchical federated learning via a tree-structured supersample construction that recover CMI bounds for bounded losses and match the asymptotic rate in the Gaussian location model.
Optimal Transport for Applied Mathematicians – Calculus of Variations, PDEs and Modeling
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A Hierarchical Sampling Framework for bounding the Generalization Error of Federated Learning
The paper derives Wasserstein-based generalization bounds for hierarchical federated learning via a tree-structured supersample construction that recover CMI bounds for bounded losses and match the asymptotic rate in the Gaussian location model.