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New framework \alg secures autonomous vehicle FRL against poisoning attacks

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new framework called \alg to enhance the security of federated reinforcement learning (FRL) systems used in autonomous vehicles. This framework addresses the threat of poisoning attacks, which can compromise the global control model by injecting malicious parameters. \alg integrates digital twins for rehearsal-based learning and uses historical data to ensure only benign information is aggregated, thereby mitigating the impact of malicious agents. The system's effectiveness has been theoretically guaranteed and validated through simulations in realistic highway environments. AI

IMPACT Enhances the security and reliability of AI systems in safety-critical applications like autonomous driving.

RANK_REASON The cluster contains a research paper detailing a new framework for securing autonomous vehicle systems using federated reinforcement learning.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New framework \alg secures autonomous vehicle FRL against poisoning attacks

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The cluster contains a research paper detailing a new framework for securing autonomous vehicle systems using federated reinforcement learning.
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2 independent sources
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paper, safety, other
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High
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52 days old
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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Zifan Zhang, Minghong Fang, Dianwei Chen, Zhuqing Liu, Prashant Khanduri, Xianfeng Yang, Anupam Das, Yuchen Liu ·

    Securing Autonomous Vehicle Systems via Twin-Aware Federated Reinforcement Learning

    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 vehicul…

  2. arXiv cs.LG TIER_1 English(EN) · Yuchen Liu ·

    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…

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