Cooperative Intelligent Transport System (C-ITS) is a promising network to provide safety, efficiency, sustainability, and comfortable services for automated vehicles and road infrastructures by taking advantages from participants. However, the components of C-ITS usually generate large amounts of data, which makes it difficult to explore data science. Currently, federated learning has been proposed as an appealing approach to allow users to cooperatively reap the benefits from trained participants. Therefore, in this paper, we propose a novel Semi-asynchronous Hierarchical Federated Learning (SHFL) framework for C-ITS that enables elastic edge to cloud model aggregation from data sensing. We further formulate a joint edge node association and resource allocation problem under the proposed SHFL framework to prevent personalities of heterogeneous road vehicles and achieve communication-efficiency. To deal with our proposed Mixed integer nonlinear programming (MINLP) problem, we introduce a distributed Alternating Direction Method of Multipliers (ADMM)-Block Coordinate Update (BCU) algorithm. With this algorithm, a tradeoff between training accuracy and transmission latency has been derived. Numerical results demonstrate the advantages of the proposed algorithm in terms of training overhead and model performance.
翻译:合作智能运输系统(C-ITS)是一个大有希望的网络,通过利用参与者的优势,为自动化车辆和道路基础设施提供安全、效率、可持续性和舒适的服务;然而,C-ITS的各组成部分通常产生大量数据,因此难以探索数据科学;目前,提议采用联合会式学习,作为一种吸引办法,使用户能够合作从经过培训的参与者那里获取好处;因此,在本文件中,我们提议为C-ITS建立一个新的半非同步的分级分级联邦学习框架,使C-ITS能够从数据遥感中得出云层模型集成弹性边缘;我们在拟议的SHMFL框架下进一步制定联合边缘节点联系和资源分配问题,以防止混杂道路车辆的性能并实现通信效率;为处理我们拟议的混合非线性编程问题,我们采用了分布式的多端线-分级协调协调算法(SHFLL)(BCU)算法,由此得出了培训精度和传输延迟度模型之间的折算法。