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cs.LG2026
A Hierarchical Sampling Framework for bounding the Generalization Error of Federated Learning
Dario Filatrella, Ragnar Thobaben, Mikael Skoglund
We study expected generalization bounds for the Hierarchical Federated Learning (HFL) setup using Wasserstein distance. We introduce a generalized framework in which data is sample…
cs.LG2024
A Coding-Theoretic Analysis of Hyperspherical Prototypical Learning Geometry
Martin Lindström, Borja Rodríguez-Gálvez, Ragnar Thobaben +1
Hyperspherical Prototypical Learning (HPL) is a supervised approach to representation learning that designs class prototypes on the unit hypersphere. The prototypes bias the repres…