Securing Autonomous Vehicle Systems via Twin-Aware Federated Reinforcement Learning
Federated reinforcement learning (FRL) is crucial for enabling collaborative learning across multiple agents without sharing raw data, thereby enhancing privacy and scalability in the decision-making process within dynamic vehicular environments. However, poisoning attacks pose a significant threat to the security and reliability of FRL-based systems, particularly in safety-critical autonomous driving, where this vulnerability remains largely unexplored. These attacks can compromise the global control model by subtly injecting malicious system parameters, leading to potential hazards. To counter these challenges, we present \alg (\underline{Sec}ure \underline{A}ggregation with \underline{...
| Authors | Zifan Zhang, Minghong Fang, Dianwei Chen et al. |
|---|---|
| Primary category | cs.CR |
| Published | 9 July 2026 |
| Last updated at source | 9 July 2026 |
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