11 citations · 17 across the 5 of their papers we have counts for
5 papers
Data-Driven Power Modeling and Monitoring via Hardware Performance Counter Tracking
Sergio Mazzola, Gabriele Ara, Thomas Benz +3
Energy-centric design is paramount in the current embedded computing era: use cases require increasingly high performance at an affordable power budget, often under real-time const…
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…
Data-Driven Power Modeling and Monitoring via Hardware Performance Counters Tracking
Sergio Mazzola, Gabriele Ara, Thomas Benz +3
In the current high-performance and embedded computing era, full-stack energy-centric design is paramount. Use cases require increasingly high performance at an affordable power bu…
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…