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Real-time End-to-End Federated Learning: An Automotive Case Study
Chalmers Univ Technol, Gothenburg, Sweden..
Chalmers Univ Technol, Gothenburg, Sweden..
Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).ORCID iD: 0000-0002-7700-1816
2021 (English)In: 2021 IEEE 45TH ANNUAL COMPUTERS, SOFTWARE, AND APPLICATIONS CONFERENCE (COMPSAC 2021) / [ed] Chan, WK Claycomb, B Takakura, H Yang, JJ Teranishi, Y Towey, D Segura, S Shahriar, H Reisman, S Ahamed, SI, IEEE, 2021, p. 459-468Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
IEEE, 2021. p. 459-468
Series
Proceedings International Computer Software and Applications Conference, ISSN 0730-3157
Keywords [en]
Federated Learning, Machine learning, Heterogeneous computation, Software Engineering
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:mau:diva-47255DOI: 10.1109/COMPSAC51774.2021.00070ISI: 000706529000059Scopus ID: 2-s2.0-85115876520ISBN: 978-1-6654-2463-9 (electronic)ISBN: 978-1-6654-2464-6 (print)OAI: oai:DiVA.org:mau-47255DiVA, id: diva2:1617376
Conference
45th Annual International IEEE-Computer-Society Computers, Software, and Applications Conference (COMPSAC), JUL 12-16, 2021, ELECTR NETWORK
Available from: 2021-12-06 Created: 2021-12-06 Last updated: 2024-02-05Bibliographically approved

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Olsson, Helena Holmström

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