Attribute Inference Attacks for Federated Regression Tasks - Inria - Institut national de recherche en sciences et technologies du numérique
Communication Dans Un Congrès Année : 2025

Attribute Inference Attacks for Federated Regression Tasks

Résumé

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 attribute inference attacks (AIA), where adversaries exploit exchanged messages and auxiliary public information to uncover sensitive attributes of targeted clients. While these attacks have been extensively studied in the context of classification tasks, their impact on regression tasks remains largely unexplored. In this paper, we address this gap by proposing novel modelbased AIAs specifically designed for regression tasks in FL environments. Our approach considers scenarios where adversaries can either eavesdrop on exchanged messages or directly interfere with the training process. We benchmark our proposed attacks against state-of-the-art methods using real-world datasets. The results demonstrate a significant increase in reconstruction accuracy, particularly in heterogeneous client datasets, a common scenario in FL. The efficacy of our model-based AIAs makes them better candidates for empirically quantifying privacy leakage for federated regression tasks.
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Dates et versions

hal-04878082 , version 1 (10-01-2025)

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Identifiants

  • HAL Id : hal-04878082 , version 1

Citer

Francesco Diana, Othmane Marfoq, Chuan Xu, Giovanni Neglia, Frédéric Giroire, et al.. Attribute Inference Attacks for Federated Regression Tasks. 39th Annual AAAI Conference on Artificial Intelligence - AAAI 2025, Feb 2025, Philadelphia (Pennsylvania), United States. ⟨hal-04878082⟩
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