Distributed federated learning among vehicles improves safety-message anomaly detection over local-only training, but jamming and label-poisoning attacks can sharply reduce accuracy.
van der Hei, Arnaud Kaiser, Pascal Urien, and Frank Kargl
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.NI 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Distributed Federated Learning for Vehicular Network Security: Anomaly Detection Benefits and Multi-Domain Attack Threats
Distributed federated learning among vehicles improves safety-message anomaly detection over local-only training, but jamming and label-poisoning attacks can sharply reduce accuracy.