边缘计算
计算机科学
建筑
车载自组网
GSM演进的增强数据速率
智能交通系统
光学(聚焦)
车载通信系统
系统体系结构
计算机体系结构
分布式计算
人工智能
工程类
无线自组网
运输工程
电信
无线
艺术
物理
光学
视觉艺术
作者
Xinran Zhang,Jingyuan Liu,T. Hu,Zheng Chang,Yanru Zhang,Geyong Min
出处
期刊:IEEE Vehicular Technology Magazine
[Institute of Electrical and Electronics Engineers]
日期:2023-12-01
卷期号:18 (4): 75-84
被引量:1
标识
DOI:10.1109/mvt.2023.3297793
摘要
Recently, realizing machine learning (ML)-based technologies with the aid of mobile edge computing (MEC) in the vehicular network to establish an intelligent transportation system (ITS) has gained considerable interest. To fully utilize the data and onboard units of vehicles, it is possible to implement federated learning (FL), which can locally train the model and centrally aggregate the results, in the vehicular edge computing (VEC) system for a vision of connected and autonomous vehicles. In this article, we review and present the concept of FL and introduce a general architecture of FL-assisted VEC to advance development of FL in the vehicular network. The enabling technologies for designing such a system are discussed and, with a focus on the vehicle selection algorithm, performance evaluations are conducted. Recommendations on future research directions are highlighted as well.
科研通智能强力驱动
Strongly Powered by AbleSci AI