1 citations · 3 across the 6 of their papers we have counts for
3 papers · 1 filter
Wavelet Scattering Transform and Fourier Representation for Offline Detection of Malicious Clients in Federated Learning
Alessandro Licciardi, Davide Leo, Davide Carbone
Federated Learning (FL) enables the training of machine learning models across decentralized clients while preserving data privacy. However, the presence of anomalous or corrupted…
Tackling Heterogeneity in Federated Learning via Variance-Reduced Boltzmann Sampling within Homogeneous Social Coalitions
Alessandro Licciardi, Roberta Raineri, Anton Proskurnikov +2
Federated Learning (FL) enables privacy-preserving collaborative model training, but its effectiveness is often limited by client data heterogeneity. We introduce a client-selectio…
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…