11 citations · 11 across the 3 of their papers we have counts for
3 papers
cs.CR2024
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…
cs.PF2024
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…
cs.LG2023★ 11 cited
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…