Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Federated Learning to Enable Automotive Collaborative Ecosystem: Opportunities and Challenges
RISE Research Institutes of Sweden, Digital Systems, Mobility and Systems.ORCID iD: 0000-0001-9808-1483
RISE Research Institutes of Sweden, Digital Systems, Mobility and Systems.ORCID iD: 0000-0003-2772-4351
RISE Research Institutes of Sweden, Digital Systems, Mobility and Systems.ORCID iD: 0000-0002-1043-8773
2020 (English)In: Proceedings of Virtual ITS European Congress, 2020, article id Paper number ITS-TP18524Conference paper, Published paper (Refereed)
Abstract [en]

Despite the strong interests in creating data economy, automotive industries are creating data silos with each stakeholder maintaining its own data cloud. Federated learning (FL), designed for privacy-preserving collaborative Machine Learning (ML), offers a promising method that allows multiple stakeholders to share information through ML models without the exposure of raw data, thus natively protecting privacy. Motivated by the strong need for automotive collaboration and the advancement of FL, this paper investigates how FL could enable privacy-preserving information sharing for automotive industries. We first introduce the statuses and challenges for automotive data sharing, followed by a brief introduction to FL. We then present a comprehensive discussion on potential applications of federated learning to enable an automotive collaborative ecosystem. To illustrate the benefits, we apply FL for driver action classification and demonstrate the potential for collaborative machine learning without data sharing.

Place, publisher, year, edition, pages
2020. article id Paper number ITS-TP18524
Keywords [en]
automotive data sharing, federated learning, privacy-preserving
National Category
Transport Systems and Logistics Information Systems Communication Systems
Identifiers
URN: urn:nbn:se:ri:diva-56294OAI: oai:DiVA.org:ri-56294DiVA, id: diva2:1590504
Conference
Virtual ITS European Congress, 9-10 November 2020
Funder
VinnovaAvailable from: 2021-09-02 Created: 2021-09-02 Last updated: 2024-05-22Bibliographically approved

Open Access in DiVA

fulltext(2938 kB)622 downloads
File information
File name FULLTEXT01.pdfFile size 2938 kBChecksum SHA-512
ce9dc42620549344890fad6dd26ad3b6b4a7b9dd8de1e756733f549e7044c285c2b2a3a6baf0deea859cac823f759603b3f67c4dc6739034b934671e9494488b
Type fulltextMimetype application/pdf

Authority records

Chen, LeiTorstensson, MartinEnglund, Cristofer

Search in DiVA

By author/editor
Chen, LeiTorstensson, MartinEnglund, Cristofer
By organisation
Mobility and Systems
Transport Systems and LogisticsInformation SystemsCommunication Systems

Search outside of DiVA

GoogleGoogle Scholar
Total: 623 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 1435 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf