2021/03/22 by Hongyi Zhang, Jan Bosch, Zhang, Hongyi +3 · 1 citation
Computer Science · Engineering · #Age of Information Optimization #Distributed #FOS: Computer and information sciences #Machine Learning (cs.LG) #Parallel #Privacy-Preserving Technologies in Data #Software Engineering (cs.SE) #Vehicular Ad Hoc Networks (VANETs) #and Cluster Computing (cs.DC) #cs.DC #cs.LG #cs.SE
paper · pdf · doi:10.48550/arxiv.2103.11879
openalex publication_date 2021/03/22 · arxiv created 2021/09/13 · arxiv updated 2022/01/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
With the development and the increasing interests in ML/DL fields, companies are eager to apply Machine Learning/Deep Learning approaches to increase service quality and customer experience. Federated Learning was implemented as an effective model training method for distributing and accelerating time-consuming model training while protecting user data privacy. However, common Federated Learning approaches, on the other hand, use a synchronous protocol to conduct model aggregation, which is inflexible and unable to adapt to rapidly changing environments and heterogeneous hardware settings in real-world scenarios. In this paper, we present an approach to real-time end-to-end Federated Learning combined with a novel asynchronous model aggregation protocol. Our method is validated in an industrial use case in the automotive domain, focusing on steering wheel angle prediction for autonomous driving. Our findings show that asynchronous Federated Learning can significantly improve the prediction performance of local edge models while maintaining the same level of accuracy as centralized machine learning. Furthermore, by using a sliding training window, the approach can minimize communication overhead, accelerate model training speed and consume real-time streaming data, proving high efficiency when deploying ML/DL components to heterogeneous real-world embedded systems.