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.
FedMultiEmo: Real-Time Emotion Recognition via Multimodal Federated Learning
Institute of Industrial Automation and Software Engineering, University of Stuttgart, Germany（工业自动化与软件工程研究所，斯图加特大学）；Electrical Engineering, University of Stuttgart, Germany（电气工程，斯图加特大学）；Graduate School of Excellence advanced Manufacturing Engineering, University of Stuttgart, Germany（先进制造工程优秀研究生院，斯图加特大学）
Real Time Semantic Segmentation of High Resolution Automotive LiDAR Scans
Faculty of Engineering, University of Applied Sciences Aschaffenburg（应用科学阿施芬堡大学工程学院）；Intelligent Embedded Systems Lab, University of Kassel（卡塞尔大学智能嵌入式系统实验室）
RadarScenes: A Real-World Radar Point Cloud Data Set for Automotive Applications
Mercedes-Benz AG（梅赛德斯-奔驰股份公司）；Continental AG（大陆集团）；Daimler AG（戴姆勒股份公司）；TU Dortmund（多特蒙德工业大学）；Faculty of Electrical Engineering & Information Technology, TU Dortmund（多特蒙德工业大学电气工程与信息技术学院）
University of California, Davis（加州大学戴维斯分校）；Arm（ARM公司）；Samsung（三星）；Qualcomm（高通）；California State University, Sacramento（加州州立大学萨克拉门托分校）；Gilmet Labs（Gilmet实验室）；NVIDIA（英伟达）；AMD（超微半导体）
