3 papers
cs.LG2026
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…
cs.LG2025
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…
cs.LG2025
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…