With the development and the increasing interests in ML/DL fields, companies are eager to utilize these methods to improve their service quality and user experience. Federated Learning has been introduced as an efficient model training approach to distribute and speed up time-consuming model training and preserve user data privacy. However, common Federated Learning methods apply a synchronized protocol to perform model aggregation, which turns out to be inflexible and unable to adapt to rapidly evolving environments and heterogeneous hardware settings in real-world systems. In this paper, we introduce an approach to real-time end-to-end Federated Learning combined with a novel asynchronous model aggregation protocol. We validate our approach in an industrial use case in the automotive domain focusing on steering wheel angle prediction for autonomous driving. Our results show that asynchronous Federated Learning can significantly improve the prediction performance of local edge models and reach the same accuracy level as the centralized machine learning method. Moreover, the approach can reduce the communication overhead, accelerate model training speed and consume real-time streaming data by utilizing a sliding training window, which proves high efficiency when deploying ML/DL components to heterogeneous real-world embedded systems.
翻译:随着ML/DL领域的发展和日益增长的兴趣,各公司渴望利用这些方法来提高服务质量和用户经验,采用联邦学习作为一种有效的示范培训方法来分发和加快耗时模式培训并保护用户数据隐私,但是,共同的联邦学习方法采用同步协议来进行模型汇总,结果显示,联邦学习方法不灵活,无法适应现实世界系统中迅速变化的环境和多样化的硬件环境。在本文件中,我们采用一种办法,结合一个新颖的不同步模式汇总协议,实时端到端的联邦学习方法。我们在汽车领域的工业使用案例中验证了我们的方法,重点是自动驾驶方向轮预测。我们的结果显示,无节奏的联邦学习方法可以大大改进当地边缘模型的预测性能,达到与中央机器学习方法相同的精确水平。此外,这一方法可以减少通信间接费用,加快模式培训速度,并通过使用滑动式培训窗口来消耗实时流数据,这证明在将ML/DL组件安装到混成现实世界嵌入系统时效率很高。