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Francesco Diana

2 accepted papers

2025

Attribute Inference Attacks for Federated Regression Tasks

AAAI 2025technical

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. However, recent studies have revealed that the training phase of FL is vulnerable to reconstruction attacks, such as a…

2025

Cutting Through Privacy: A Hyperplane-Based Data Reconstruction Attack in Federated Learning

UAI 2025

Federated Learning (FL) enables collaborative training of machine learning models across distributed clients without sharing raw data, ostensibly preserving data privacy. Nevertheless, recent studies have revealed critical vulnerabilities in FL, showing that a malicious central server can manipulate

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