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LFighter: Defending against the label-flipping attack in federated learning.

Najeeb Moharram Jebreel1, Josep Domingo-Ferrer1, David Sánchez1

  • 1Universitat Rovira i Virgili, Department of Computer Engineering and Mathematics, CYBERCAT Center for Cybersecurity Research of Catalonia, UNESCO Chair in Data Privacy, Av. Països Catalans 26, E-43007 Tarragona, Catalonia.

Neural Networks : the Official Journal of the International Neural Network Society
|November 17, 2023
PubMed
Summary

Federated learning (FL) is vulnerable to label-flipping (LF) attacks. We introduce LFighter, a novel defense that detects and filters malicious updates by analyzing model parameter gradients, enhancing global model performance and accuracy.

Keywords:
Deep learning modelsFederated learningLabel-flipping attacksPoisoning attacksSecurity

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Area of Science:

  • Machine Learning
  • Cybersecurity
  • Distributed Systems

Background:

  • Federated learning (FL) enables collaborative model training while preserving data privacy.
  • Malicious participants can compromise FL models through poisoning attacks, such as label-flipping (LF).
  • Existing LF defenses face limitations regarding data distribution assumptions and high-dimensional models.

Purpose of the Study:

  • To investigate the behavior of label-flipping attacks in federated learning.
  • To propose a novel defense mechanism, LFighter, against label-flipping attacks.
  • To evaluate the effectiveness and superiority of LFighter compared to existing defenses.

Main Methods:

  • Investigated the impact of LF attacks on parameter gradients of source and target classes.
  • Developed LFighter to dynamically extract, cluster, and analyze gradients from local updates.
  • Filtered out malicious updates before global model aggregation.

Main Results:

  • LFighter effectively detects label-flipping attacks by leveraging gradient features.
  • The defense demonstrates robustness across different data distributions and model dimensionalities.
  • LFighter significantly outperforms state-of-the-art defenses in accuracy, error rates, and stability.

Conclusions:

  • Parameter gradients offer discriminative features for detecting label-flipping attacks in FL.
  • LFighter provides an effective and robust defense against label-flipping attacks.
  • The proposed method enhances the security and reliability of federated learning systems.