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

Kunal Bhatnagar, Sagana Chattanathan, Angela Dang, Bhargav Eranki, Ronnit Rana, Charan Sridhar, Siddharth Vedam, Angie Yao and Mark Stamp ()
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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

A chapter in Machine Learning, Deep Learning and AI for Cybersecurity, 2025, pp 433-454 from Springer

Abstract: 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.

Date: 2025
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DOI: 10.1007/978-3-031-83157-7_15

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