1 citations · 3 across the 6 of their papers we have counts for
6 papers
Domain Generalization using Action Sequences for Egocentric Action Recognition
Amirshayan Nasirimajd, Chiara Plizzari, Simone Alberto Peirone +3
Recognizing human activities from visual inputs, particularly through a first-person viewpoint, is essential for enabling robots to replicate human behavior. Egocentric vision, cha…
FedDiverse: Tackling Data Heterogeneity in Federated Learning with Diversity-Driven Client Selection
Gergely D. Németh, Eros Fanì, Yeat Jeng Ng +4
Federated Learning (FL) enables decentralized training of machine learning models on distributed data while preserving privacy. However, in real-world FL settings, client data is o…
Interaction-Aware Gaussian Weighting for Clustered Federated Learning
Alessandro Licciardi, Davide Leo, Eros Fanì +2
Federated Learning (FL) emerged as a decentralized paradigm to train models while preserving privacy. However, conventional FL struggles with data heterogeneity and class imbalance…
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…
Accelerating Heterogeneous Federated Learning with Closed-form Classifiers
Eros Fanì, Raffaello Camoriano, Barbara Caputo +1
Federated Learning (FL) methods often struggle in highly statistically heterogeneous settings. Indeed, non-IID data distributions cause client drift and biased local solutions, par…
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…