Real-time end-to-end federated learning: An automotive case study
Paper i proceeding, 2021

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.

Machine learning

Heterogeneous computation

Software engineering

Federated learning

Författare

Hongyi Zhang

Testing, Requirements, Innovation and Psychology

Jan Bosch

Testing, Requirements, Innovation and Psychology

Helena Holmström Olsson

Malmö universitet

Proceedings - 2021 IEEE 45th Annual Computers, Software, and Applications Conference, COMPSAC 2021

459-468
9781665424639 (ISBN)

45th IEEE Annual Computers, Software, and Applications Conference, COMPSAC 2021
Virtual, Online, Spain,

Ämneskategorier

Annan data- och informationsvetenskap

Datavetenskap (datalogi)

Datorsystem

DOI

10.1109/COMPSAC51774.2021.00070

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Senast uppdaterat

2024-01-03