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arXiv AI By Yalin E. Sagduyu, Tugba Erpek, Nathan Stephenson, Mengran Xue

Clean-label Poisoning Attack and Defense Game for Federated Learning in Vehicular Networks

Read the original on arXiv AI →

This paper investigates clean‑label poisoning attacks and defenses in federated learning for vehicular networks, focusing on Basic Safety Message classification for anomaly detection. It introduces a feature‑space attack that perturbs benign samples toward malicious extremes, reducing class separability while keeping labels intact, and proposes a feature‑consistency relabeling defense to detect and correct suspicious samples. A game‑theoretic framework models attack‑defense interactions, showing that the attack scales with compromised nodes but the defense maintains low attack success and clean accuracy, shifting equilibrium toward attack‑averse regimes.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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