A Federated Learning Framework Based on Incremental Weighting and Diversity Selection for Internet of Vehicles Article Swipe
Lei Yuan
,
Shir Li Wang
,
Minghui Zhong
,
Meixia Wang
,
Theam Foo Ng
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/electronics11223668
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/electronics11223668
With the rapid increase of data, centralized machine learning can no longer meet the application requirements of the Internet of Vehicles (IoV). On the one hand, both car owners and regulators pay more attention to data privacy and are unwilling to share data, which forms the isolated data island challenge. On the other hand, the incremental data generated in IoV are massive and diverse. All these issues have brought challenges of data increment and data diversity. The current common federated learning or incremental learning frameworks cannot effectively integrate incremental data with existing machine learning (ML) models. Therefore, this paper proposes a Federated Learning Framework Based on Incremental Weighting and Diversity Selection for IoV (Fed-IW&DS). In Fed-IW&DS, a vehicle diversity selection algorithm was proposed, which uses a variety of performance indicators to calculate diversity scores, effectively reducing homogeneous computing. Also, it proposes a vehicle federated incremental algorithm that uses an improved arctangent curve as the decay function, to realize the rapid fusion of incremental data with existing ML models. Moreover, we have carried out several sets of experiments to test the validity of the proposed Fed-IW&DS framework’s performance. The experimental results show that, under the same global communication round and similar computing time, the Fed-IW&DS framework has significantly improved performance in all aspects compared to the frameworks FED-AVG, FED-SGD, FED-prox & the decay functions linear, square curve and arc tangent. Specifically, the Fed-IW&DS framework improves the Acc (accuracy), loss (loss), and Matthews correlation coefficient (MCC) by approximately 32%, 83%, and 66%, respectively. This result shows that Fed-IW&DS is a more reliable solution than the common frameworks of federated learning, and it can effectively deal with the dynamic incremental data in the IoV scenario. Our findings should make a significant contribution to the field of federated learning.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/electronics11223668
- https://www.mdpi.com/2079-9292/11/22/3668/pdf?version=1669018019
- OA Status
- gold
- Cited By
- 21
- References
- 41
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4308870594
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4308870594Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/electronics11223668Digital Object Identifier
- Title
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A Federated Learning Framework Based on Incremental Weighting and Diversity Selection for Internet of VehiclesWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
-
2022-11-09Full publication date if available
- Authors
-
Lei Yuan, Shir Li Wang, Minghui Zhong, Meixia Wang, Theam Foo NgList of authors in order
- Landing page
-
https://doi.org/10.3390/electronics11223668Publisher landing page
- PDF URL
-
https://www.mdpi.com/2079-9292/11/22/3668/pdf?version=1669018019Direct link to full text PDF
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2079-9292/11/22/3668/pdf?version=1669018019Direct OA link when available
- Concepts
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Weighting, Computer science, Selection (genetic algorithm), The Internet, Machine learning, Homogeneous, Artificial intelligence, Algorithm, Data mining, Mathematics, Operating system, Radiology, Combinatorics, MedicineTop concepts (fields/topics) attached by OpenAlex
- Cited by
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21Total citation count in OpenAlex
- Citations by year (recent)
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2025: 4, 2024: 8, 2023: 9Per-year citation counts (last 5 years)
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41Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.32%, | 246 |
| abstract_inverted_index.66%, | 249 |
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