计算机科学
隐藏物
马尔可夫决策过程
强化学习
GSM演进的增强数据速率
可靠性(半导体)
边缘设备
边缘计算
过程(计算)
深度学习
信息隐私
方案(数学)
分布式计算
实时计算
人工智能
计算机网络
马尔可夫过程
云计算
计算机安全
功率(物理)
操作系统
数学分析
物理
统计
量子力学
数学
作者
Chunlin Li,Yong Zhang,Yingwei Luo
出处
期刊:IEEE Transactions on Intelligent Transportation Systems
[Institute of Electrical and Electronics Engineers]
日期:2023-03-01
卷期号:24 (3): 3360-3369
被引量:16
标识
DOI:10.1109/tits.2022.3224395
摘要
Massive map data transmission and the strict demand for the privacy of high-precision maps have brought significant challenges to the cache of high-precision maps in intelligent connected vehicles (ICV). Federal learning (FL) was introduced to reduce the pressure on the edge network and protect privacy. But the high dynamics of cars and limited resources lead to low accuracy and high training delay. We propose a joint optimization scheme of participant selection and resource allocation for federated learning. In each time slice, vehicles are determined whether to participate in training, which minimizes long-term training delay with limited energy consumption. To meet the delay and privacy requirements of high-precision map caching, we present an edge cooperative caching scheme based on federated deep reinforcement learning (F-DRL), which aims to achieve dynamic adaptive edge caching while protecting user privacy. The collaborative caching model is formulated as a Markov decision process (MDP). Dueling Deep Q Network (Dueling-DQN) is used to solve the optimal strategy, and federal learning is used for training. Enough comparative experiments to evaluate the performance of the proposed schemes. The aspects of reliability, cache hit rate, and training accuracy prove that the method effectively improves the training parameters of federated learning while meeting a high-precision map cache’s delay and reliability requirements.
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