arXiv:2607.08137v1 Announce Type: cross Abstract: 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
The increasing deployment of autonomous vehicles necessitates robust security solutions, particularly as federated learning becomes a prominent architecture for their development.
Securing AI architectures like federated learning in safety-critical applications like autonomous driving is crucial for public trust, regulatory acceptance, and widespread adoption.
This research introduces methods to mitigate poisoning attacks in federated reinforcement learning for autonomous vehicles, enhancing trust and reliability in their AI systems.
- · Autonomous Vehicle Manufacturers
- · Cybersecurity Firms
- · AI/ML Developers
- · Transportation Sector
- · Malicious Actors
- · Unsecured AI Systems
- · Traditional Centralized Learning Methods
Enhanced security protocols for autonomous vehicle AI systems will be developed and implemented.
Increased public confidence in autonomous vehicle safety could accelerate their deployment and regulatory approval.
The methodology could be extended to secure other safety-critical AI systems, broadening its impact beyond autonomous driving.
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Read at arXiv cs.LG