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An Empirical Analysis of Federated Learning Models Subject to Label-Flipping Adversarial Attack

In: Machine Learning, Deep Learning and AI for Cybersecurity

Author

Listed:
  • Kunal Bhatnagar

    (San Jose State University)

  • Sagana Chattanathan

    (San Jose State University)

  • Angela Dang

    (San Jose State University)

  • Bhargav Eranki

    (San Jose State University)

  • Ronnit Rana

    (San Jose State University)

  • Charan Sridhar

    (San Jose State University)

  • Siddharth Vedam

    (San Jose State University)

  • Angie Yao

    (San Jose State University)

  • Mark Stamp

    (San Jose State University)

Abstract

In this paper, we empirically analyze adversarial attacks on selected Federated Learning (FL) models. The specific models considered are FL versions of Multinominal Logistic Regression (MLR), Support Vector Classifier (SVC), Multilayer Perceptron (MLP), Convolution Neural Network (CNN), Random Forest, XGBoost, and Long Short-Term Memory (LSTM). For each model, we simulate label-flipping attacks, experimenting extensively with 10 federated clients and 100 federated clients. We vary the percentage of adversarial clients from 10 to 100% and, simultaneously, the percentage of labels flipped by each adversarial client is also varied from 10 to 100%. Among other results, we find that models differ in their inherent robustness to the two vectors in our label-flipping attack, i.e., the percentage of adversarial clients, and the percentage of labels flipped by each adversarial client. We discuss the potential practical implications of our results.

Suggested Citation

  • Kunal Bhatnagar & Sagana Chattanathan & Angela Dang & Bhargav Eranki & Ronnit Rana & Charan Sridhar & Siddharth Vedam & Angie Yao & Mark Stamp, 2025. "An Empirical Analysis of Federated Learning Models Subject to Label-Flipping Adversarial Attack," Springer Books, in: Mark Stamp & Martin Jureček (ed.), Machine Learning, Deep Learning and AI for Cybersecurity, pages 433-454, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-83157-7_15
    DOI: 10.1007/978-3-031-83157-7_15
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