Deep-Reinforcement-Learning-Based Latency Minimization in Edge Intelligence Over Vehicular Networks

【Author】 Zhao, Ning; Wu, Hao; Yu, F. Richard; Wang, Lifu; Zhang, Weiting; Leung, Victor C. M.

【Source】IEEE INTERNET OF THINGS JOURNAL

【影响因子】10.238

【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

【发表时间】2022 15-Jan

【收录时间】2022-01-18

【文献类型】期刊

【主题类别】

区块链技术--