id:"swepub:oai:research.chalmers.se:4d3ba3e1-7fd7-41d9-8504-d910284c9a7e"
Search: id:"swepub:oai:research.chalmers.se:4d3ba3e1-7fd7-41d9-8504-d910284c9a7e" > QuaFedAsync: Qualit...
- 1 of 1
- Previous record
- Next record
- To hitlist
QuaFedAsync: Quality-based Asynchronous Federated Learning for the Embedded Systems
-
- Zhang, Hongyi, 1996 (author)
- Chalmers tekniska högskola,Chalmers University of Technology,Chalmers University of Technology, Gothenburg, Sweden
-
- Bosch, Jan, 1967 (author)
- Chalmers tekniska högskola,Chalmers University of Technology,Chalmers University of Technology, Gothenburg, Sweden
-
- Olsson, Helena Holmström (author)
- Malmö universitet,Institutionen för datavetenskap och medieteknik (DVMT),Malmö university
- (creator_code:org_t)
- Institute of Electrical and Electronics Engineers (IEEE), 2023
- 2023
- English.
- In: Proceedings - 2023 49th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2023. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 70-73
- Conference paper (peer-reviewed)
Abstract
Subject headings
Close
- In recent years, Federated Learning, as an approach to distributed learning, has shown its potential with the increasing number of devices on the edge and the development of computing power. The method enables large-scale training on the device that creates the data but with the sensitive data remaining within the data's owner. In reality, however, the vast majority of enterprises have the problem of low data volume and poor model quality to support the implementation of Federated Learning methods. Learning quality assurance for edge devices is still the major issue which prevents Federated Learning to be applied in industrial contexts, especially in safety-critical applications. In this paper, we propose a quality-based asynchronous Federated Learning algorithm (QuaFedAsync) to address these challenges. We report on a study in which we used two well-known data sets, i.e., DDAD and KITTI datasets, and validate the proposed algorithm on an industrial use case concerned with monocular depth estimation in the automotive domain. Our results show that the proposed algorithm significantly improves the prediction performance compared to the commonly applied aggregation protocols while maintaining the same level of accuracy as centralized machine learning. Based on the results, we prove the learning efficiency and robustness when applying the algorithm to industrial scenarios.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Datorsystem (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Computer Systems (hsv//eng)
Keyword
- Federated Learning
- Artificial Intelligence
- Machine Learning
- Quality Assurance
Publication and Content Type
- kon (subject category)
- ref (subject category)
To the university's database
- 1 of 1
- Previous record
- Next record
- To hitlist
Find more in SwePub
- By the author/editor
- Zhang, Hongyi, 1 ...
- Bosch, Jan, 1967
- Olsson, Helena H ...
- About the subject
-
- NATURAL SCIENCES
- NATURAL SCIENCES
- and Computer and Inf ...
- and Computer Science ...
-
- ENGINEERING AND TECHNOLOGY
- ENGINEERING AND ...
- and Electrical Engin ...
- and Computer Systems
- Articles in the publication
- Proceedings - 20 ...
- By the university
- Chalmers University of Technology
- Malmö University
Search outside SwePub
- Extend your search to:
- Google Book Search
- Google Scholar
Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.
- Copyright © LIBRIS - National Library Systems
- LIBRIS.kb.se
