12 citations · 36 across the 5 of their papers we have counts for
5 papers
Noisy Neighbors: Efficient membership inference attacks against LLMs
Filippo Galli, Luca Melis, Tommaso Cucinotta
The potential of transformer-based LLMs risks being hindered by privacy concerns due to their reliance on extensive datasets, possibly including sensitive information. Regulatory m…
Online Sensitivity Optimization in Differentially Private Learning
Filippo Galli, Catuscia Palamidessi, Tommaso Cucinotta
Training differentially private machine learning models requires constraining an individual's contribution to the optimization process. This is achieved by clipping the -norm of…
Advancing Personalized Federated Learning: Group Privacy, Fairness, and Beyond
Filippo Galli, Kangsoo Jung, Sayan Biswas +2
Federated learning (FL) is a framework for training machine learning models in a distributed and collaborative manner. During training, a set of participating clients process their…
Group privacy for personalized federated learning
Filippo Galli, Sayan Biswas, Kangsoo Jung +2
Federated learning (FL) is a type of collaborative machine learning where participating peers/clients process their data locally, sharing only updates to the collaborative model. T…
Predictive Auto-scaling with OpenStack Monasca
Giacomo Lanciano, Filippo Galli, Tommaso Cucinotta +2
Cloud auto-scaling mechanisms are typically based on reactive automation rules that scale a cluster whenever some metric, e.g., the average CPU usage among instances, exceeds a pre…