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cs.LG2025
Robustness of Selected Learning Models under Label-Flipping Attack
Sarvagya Bhargava, Mark Stamp
In this paper we compare traditional machine learning and deep learning models trained on a malware dataset when subjected to adversarial attack based on label-flipping. Specifical…
cs.LG2025
Temporal Analysis of Adversarial Attacks in Federated Learning
Rohit Mapakshi, Sayma Akther, Mark Stamp
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 significan…
cs.LG2024
An Empirical Analysis of Federated Learning Models Subject to Label-Flipping Adversarial Attack
Kunal Bhatnagar, Sagana Chattanathan, Angela Dang +6
In this paper, we empirically analyze adversarial attacks on selected federated learning models. The specific learning models considered are Multinominal Logistic Regression (MLR),…