20 citations · 27 across the 6 of their papers we have counts for
8 papers
Beyond Local Sharpness: Communication-Efficient Global Sharpness-aware Minimization for Federated Learning
Debora Caldarola, Pietro Cagnasso, Barbara Caputo +1
Federated learning (FL) enables collaborative model training with privacy preservation. Data heterogeneity across edge devices (clients) can cause models to converge to sharp minim…
Collaborative Visual Place Recognition through Federated Learning
Mattia Dutto, Gabriele Berton, Debora Caldarola +3
Visual Place Recognition (VPR) aims to estimate the location of an image by treating it as a retrieval problem. VPR uses a database of geo-tagged images and leverages deep neural n…
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…
Learning Across Domains and Devices: Style-Driven Source-Free Domain Adaptation in Clustered Federated Learning
Donald Shenaj, Eros Fanì, Marco Toldo +6
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 co…
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…
FedDrive: Generalizing Federated Learning to Semantic Segmentation in Autonomous Driving
Lidia Fantauzzo, Eros Fanì, Debora Caldarola +4
Semantic Segmentation is essential to make self-driving vehicles autonomous, enabling them to understand their surroundings by assigning individual pixels to known categories. Howe…
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…