2 papers
cond-mat.dis-nn2026
A solvable model for unsupervised federated learning
Giovanni Catania, Aurélien Decelle, Gianluca Manzan +2
We introduce a theoretical framework for analyzing federated learning in a generative setting through a teacher-multiple interacting students scenario, in which each student receiv…
cond-mat.dis-nn2026
The effect of priors on Learning with Restricted Boltzmann Machines
Gianluca Manzan, Daniele Tantari
Restricted Boltzmann Machines (RBMs) are generative models designed to learn from data with a rich underlying structure. In this work, we explore a teacher-student setting where a…