20 citations · 27 across the 6 of their papers we have counts for
4 papers · 1 filter
Window-based Model Averaging Improves Generalization in Heterogeneous Federated Learning
Debora Caldarola, Barbara Caputo, Marco Ciccone
Federated Learning (FL) aims to learn a global model from distributed users while protecting their privacy. However, when data are distributed heterogeneously the learning process…
Improving Generalization in Federated Learning by Seeking Flat Minima
Debora Caldarola, Barbara Caputo, Marco Ciccone
Models trained in federated settings often suffer from degraded performances and fail at generalizing, especially when facing heterogeneous scenarios. In this work, we investigate…
Speeding up Heterogeneous Federated Learning with Sequentially Trained Superclients
Riccardo Zaccone, Andrea Rizzardi, Debora Caldarola +2
Federated Learning (FL) allows training machine learning models in privacy-constrained scenarios by enabling the cooperation of edge devices without requiring local data sharing. T…
Cluster-driven Graph Federated Learning over Multiple Domains
Debora Caldarola, Massimiliano Mancini, Fabio Galasso +3
Federated Learning (FL) deals with learning a central model (i.e. the server) in privacy-constrained scenarios, where data are stored on multiple devices (i.e. the clients). The ce…