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Temporal Analysis of Adversarial Attacks in Federated Learning

In: Machine Learning, Deep Learning and AI for Cybersecurity

Author

Listed:
  • Rohit Mapakshi

    (San Jose State University)

  • Sayma Akther

    (San Jose State University)

  • Mark Stamp

    (San Jose State University)

Abstract

In this paper, we experimentally analyze the robustness of selected Federated Learning (FL) systems in the presence of adversarial clients. We find that temporal attacks significantly affect model performance in the FL models tested, especially when the adversaries are active throughout or during the later rounds. We consider a variety of classic learning models, including Multinominal Logistic Regression (MLR), Random Forest, XGBoost, Support Vector Classifier (SVC), as well as various Neural Network models including Multilayer Perceptron (MLP), Convolution Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM). Our results highlight the effectiveness of temporal attacks and the need to develop strategies to make the FL process more robust against such attacks. We also briefly consider the effectiveness of defense mechanisms, including outlier detection in the aggregation algorithm.

Suggested Citation

  • Rohit Mapakshi & Sayma Akther & Mark Stamp, 2025. "Temporal Analysis of Adversarial Attacks in Federated Learning," Springer Books, in: Mark Stamp & Martin Jureček (ed.), Machine Learning, Deep Learning and AI for Cybersecurity, pages 359-392, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-83157-7_13
    DOI: 10.1007/978-3-031-83157-7_13
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