【Author】
Zhao, Ning; Wu, Hao; Yu, F. Richard; Wang, Lifu; Zhang, Weiting; Leung, Victor C. M.
【Source】IEEE INTERNET OF THINGS JOURNAL
【Abstract】A novel paradigm that combines federated learning with blockchain to empower edge intelligence over vehicular networks (FBVN) can enable latency-sensitive deep neural network-based applications to be executed in a distributed pattern. However, the complex environments in FBVN make the system latency much harder to minimize by traditional methods. In this article, we model the training and transmission latency of each autonomous vehicle (AV) and consensus latency of the blockchain in-edge side in FBVN. Considering the dynamic and time-varying wireless channel conditions, unpredictable packet error rate, and unstable data sets quality, we adopt duel deep Q-learning (DDQL) as the solving approach. We propose a federated DDQL algorithm, in which the learning agent is deployed on each AV side, and the sensing states on each AV do not need to be shared so that it increases scalability and flexibility for practical implementation. Simulation results show that the proposed algorithm has better performance in reducing system latency compared with the other schemes.
【Keywords】Blockchain; duel deep Q-learning (DDQL); edge intelligence; federated learning
【标题】车载网络边缘智能中基于深度强化学习的延迟最小化
【摘要】一种将联邦学习与区块链相结合以增强车载网络 (FBVN) 边缘智能的新范式可以使基于延迟敏感的深度神经网络的应用程序能够以分布式模式执行。然而，FBVN 中复杂的环境使得传统方法更难以最小化系统延迟。在本文中，我们对 FBVN 中每辆自动驾驶汽车 (AV) 的训练和传输延迟以及区块链边缘侧的共识延迟进行建模。考虑到动态和时变的无线信道条件、不可预测的误包率和不稳定的数据集质量，我们采用双深度 Q 学习 (DDQL) 作为解决方法。我们提出了一种联合 DDQL 算法，其中学习代理部署在每个 AV 端，每个 AV 上的感知状态不需要共享，从而增加了实际实现的可扩展性和灵活性。仿真结果表明，与其他方案相比，该算法在降低系统延迟方面具有更好的性能。
【关键词】区块链；决斗深度 Q 学习 (DDQL)；边缘智能；联邦学习
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