no code implementations • 2 Oct 2023 • Debora Caldarola, Barbara Caputo, Marco Ciccone
To address these issues and improve the robustness and generalization capabilities of the global model, we propose WIMA (Window-based Model Averaging).
1 code implementation • 5 Oct 2022 • Donald Shenaj, Eros Fanì, Marco Toldo, Debora Caldarola, Antonio Tavera, Umberto Michieli, Marco Ciccone, Pietro Zanuttigh, Barbara Caputo
Federated Learning (FL) has recently emerged as a possible way to tackle the domain shift in real-world Semantic Segmentation (SS) without compromising the private nature of the collected data.
1 code implementation • 22 Mar 2022 • 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.
1 code implementation • 28 Feb 2022 • Lidia Fantauzzo, Eros Fanì, Debora Caldarola, Antonio Tavera, Fabio Cermelli, Marco Ciccone, Barbara Caputo
For similar reasons, Federated Learning has been recently introduced as a new machine learning paradigm aiming to learn a global model while preserving privacy and leveraging data on millions of remote devices.
1 code implementation • 26 Jan 2022 • Riccardo Zaccone, Andrea Rizzardi, Debora Caldarola, Marco Ciccone, Barbara Caputo
data severely impairs both the performance of the trained neural network and its convergence rate, increasing the number of communication rounds requested to reach a performance comparable to that of the centralized scenario.
no code implementations • 29 Apr 2021 • Debora Caldarola, Massimiliano Mancini, Fabio Galasso, Marco Ciccone, Emanuele Rodolà, Barbara Caputo
Clustering may reduce heterogeneity by identifying the domains, but it deprives each cluster model of the data and supervision of others.