30 citations · 33 across the 2 of their papers we have counts for
4 papers
On the Detectability of Active Gradient Inversion Attacks in Federated Learning
Vincenzo Carletti, Pasquale Foggia, Carlo Mazzocca +2
One of the key advantages of Federated Learning (FL) is its ability to collaboratively train a Machine Learning (ML) model while keeping clients' data on-site. However, this can cr…
GUIDE: Enhancing Gradient Inversion Attacks in Federated Learning with Denoising Models
Vincenzo Carletti, Pasquale Foggia, Carlo Mazzocca +2
Federated Learning (FL) enables collaborative training of Machine Learning (ML) models across multiple clients while preserving their privacy. Rather than sharing raw data, federat…
Learning sound representations using trainable COPE feature extractors
Nicola Strisciuglio, Mario Vento, Nicolai Petkov
Sound analysis research has mainly been focused on speech and music processing. The deployed methodologies are not suitable for analysis of sounds with varying background noise, in…
Action recognition by learning pose representations
Alessia Saggese, Nicola Strisciuglio, Mario Vento +1
Pose detection is one of the fundamental steps for the recognition of human actions. In this paper we propose a novel trainable detector for recognizing human poses based on the an…