50 citations · 76 across the 10 of their papers we have counts for
9 papers · 1 filter
No More Guessing: a Verifiable Gradient Inversion Attack in Federated Learning
Francesco Diana, Chuan Xu, André Nusser +1
Gradient inversion attacks threaten client privacy in federated learning by reconstructing training samples from clients' shared gradients. Gradients aggregate contributions from m…
Reconciling Communication Compression and Byzantine-Robustness in Distributed Learning
Diksha Gupta, Antonio Honsell, Chuan Xu +2
Distributed learning enables scalable model training over decentralized data, but remains hindered by Byzantine faults and high communication costs. While both challenges have been…
Cutting Through Privacy: A Hyperplane-Based Data Reconstruction Attack in Federated Learning
Francesco Diana, André Nusser, Chuan Xu +1
Federated Learning (FL) enables collaborative training of machine learning models across distributed clients without sharing raw data, ostensibly preserving data privacy. Neverthel…
Attribute Inference Attacks for Federated Regression Tasks
Francesco Diana, Othmane Marfoq, Chuan Xu +3
Federated Learning (FL) enables multiple clients, such as mobile phones and IoT devices, to collaboratively train a global machine learning model while keeping their data localized…
Federated Learning for Collaborative Inference Systems: The Case of Early Exit Networks
Caelin Kaplan, Angelo Rodio, Tareq Si Salem +2
As Internet of Things (IoT) technology advances, end devices like sensors and smartphones are progressively equipped with AI models tailored to their local memory and computational…
Local Model Reconstruction Attacks in Federated Learning and their Uses
Ilias Driouich, Chuan Xu, Giovanni Neglia +2
In this paper, we initiate the study of local model reconstruction attacks for federated learning, where a honest-but-curious adversary eavesdrops the messages exchanged between a…