Recent advances in information technology have revolutionized the automotive industry, paving the way for next-generation smart vehicular mobility. Vehicles, roadside units, and other road users can collaborate to deliver novel services and applications. These services and applications require 1) massive volumes of heterogeneous and continuous data to perceive the environment, 2) reliable and low-latency communication networks, 3) real-time data processing that provides decision support under application-specific constraints. Addressing such constraints introduces significant challenges for current communication and computing technologies. Relatedly, the fifth generation of cellular networks (5G) was developed to respond to communication challenges by providing for low-latency, high-reliability, and high bandwidth communications. As a major part of 5G, edge computing allows data offloading and computation at the edge of the network, ensuring low-latency and context-awareness, and 5G efficiency. In this work, we aim at providing a comprehensive overview of the state of research on vehicular computing in the emerging age of 5G and big data.
Enabling Intelligent Vehicular Networks Through Distributed Learning in the Non-Terrestrial Networks 6G Vision
Alma Mater Studiorum - Università di Bologna（博洛尼亚大学）；Department of Electrical, Electronic and Information Engineering “Guglielmo Marconi”（古列尔莫·马可尼电气、电子与信息工程系）
Optimizing Age of Information in Vehicular Edge Computing with Federated Graph Neural Network Multi-Agent Reinforcement Learning
School of Internet of Things Engineering, Jiangnan University（江南大学物联网工程学院）；Tsinghua University（清华大学）；Xidian University（西安电子科技大学）；Shanghai Jiao Tong University（上海交通大学）；University of Kent（肯特大学）；Hong Kong University of Science and Technology（香港科技大学）
Towards Scalable Distributed Training of Deep Learning on Public Cloud Clusters
The Hong Kong University of Science and Technology（香港科技大学）；Tencent Ltd.（腾讯有限公司）；University of Michigan（密歇根大学）；Hong Kong Baptist University（香港浸会大学）
分布式训练技术已被广泛部署在密集GPU集群上的大规模深度神经网络（DNN）训练中。然而，在公有云集群上，由于实例之间的互连带宽适中，传统的最先进的分布式训练系统在训练大规模模型时无法很好地扩展。在本文中，我们提出了一个新的计算和通信高效的top-k稀疏通信库的分布式训练。为了进一步提高系统的可扩展性，我们提出了一个简单而有效的多级数据缓存机制优化I/O和优化更新操作，通过引入一种新的并行张量操作。在16节点的腾讯云集群（每个节点8颗Nvidia Tesla V100 GPU）上的实验结果表明，我们的系统在CNN和Transformer上的速度比现有最先进的系统快25%-40%。我们终于打破了DAWNBench在训练ResNet上的记录-ImageNet上的前5名准确率达到50%至93%。
FedRAV: Hierarchically Federated Region-Learning for Traffic Object Classification of Autonomous Vehicles
College of Computer Science, Chongqing University（重庆大学计算机学院）；School of Computer Science and Technology, Beijing Institute of Technology（北京理工大学计算机科学与技术学院）
