most citedDistributed Federated Learning for Vehicular Network Security: Anomaly Detection Benefits and Multi-Domain Attack Threats

1 citations · 1 across the 2 of their papers we have counts for

collaborators

2 papers

cs.NI2025

Targeted Attacks and Defenses for Distributed Federated Learning in Vehicular Networks

Utku Demir, Tugba Erpek, Yalin E. Sagduyu +2

In emerging networked systems, mobile edge devices such as ground vehicles and unmanned aerial system (UAS) swarms collectively aggregate vast amounts of data to make machine learn…

cs.NI2025★ 1 cited

Distributed Federated Learning for Vehicular Network Security: Anomaly Detection Benefits and Multi-Domain Attack Threats

Utku Demir, Yalin E. Sagduyu, Tugba Erpek +3

In connected and autonomous vehicles, machine learning for safety message classification has become critical for detecting malicious or anomalous behavior. However, conventional ap…