计算机科学
强化学习
异步通信
边缘计算
GSM演进的增强数据速率
计算机网络
人工智能
分布式计算
作者
Qiong Wu,Yu Zhao,Qi Fan,Pingyi Fan,Jiangzhou Wang,Cui Zhang
出处
期刊:IEEE Journal of Selected Topics in Signal Processing
[Institute of Electrical and Electronics Engineers]
日期:2023-01-01
卷期号:17 (1): 66-81
被引量:34
标识
DOI:10.1109/jstsp.2022.3221271
摘要
Vehicular edge computing (VEC) can learn and cache most popular contents for vehicular users (VUs) in the roadside units (RSUs) to support real-time vehicular applications. Federated learning (FL) can protect VUs' privacy by sharing vehicles' local models instead of data. In traditional FL, the global model is periodically updated by aggregating all vehicles' local models. However, vehicles may frequently drive out of the coverage area of VEC before they finish the local model training and thus the traditional FL cannot upload all local models as expected, which would degrade the accuracy of global model. The asynchronous FL can be performed without aggregating all vehicles' local models, thus more local models can be uploaded to improve the accuracy of global model. The vehicle mobility significantly impacts the asynchronous FL. There is no published work considering the vehicle mobility to design the cooperative caching in VEC based on asynchronous FL. In addition, the caching capacity of RSU is limited and the size of the predicted popular contents usually exceeds the cache capacity of RSU. Hence, VEC should cache the predicted popular contents in different RSUs while considering content transmission delay. In this paper, we consider vehicle mobility and propose a cooperative caching scheme in the VEC based on asynchronous federated and deep reinforcement learning (CAFR) to predict popular contents and further obtain the optimal cooperative caching location for the predicted popular contents. Extensive experimental results have demonstrated that CAFR scheme outperforms other baseline caching schemes.
科研通智能强力驱动
Strongly Powered by AbleSci AI