Peer Review #2 of "OES-Fed: a federated learning framework in vehicular network based on noise data filtering (v0.1)" Article Swipe
Lei Yuan
,
Shir Li
,
Wang Corresp
,
Caiyu Su
,
Theam Foo Ng
,
Shir Li Wang
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.7287/peerj-cs.1101v0.1/reviews/2
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.7287/peerj-cs.1101v0.1/reviews/2
Internet of Vehicles (IoV) is an interactive network providing intelligent traffic management, intelligent dynamic information service, and intelligent vehicle control to the running vehicles.One of the main problems in IoV is the reluctance of vehicles to share local data resulting in the cloud server not being able to acquire sufficient amount of data to build accurate Machine Learning (ML) models.Besides, communication efficiency and ML model accuracy in IoV are affected by noise data that being caused by violent shaking, obscuration of in-vehicle cameras.Therefore we propose a new Outlier Detection and Exponential Smoothing federated learning (OES-Fed) framework to overcome the problems.More specifically, we filter the noise data of the local ML model in the IoV from the current perspective and historical perspective.The noise data filtering is implemented by combining data outlier, K-means, Kalman filter and exponential smoothing algorithms.The experimental results of the three datasets show that the OES-Fed framework proposed in this paper achieved higher accuracy, lower loss, and better Area Under Curve (AUC) .The OES-Fed framework we proposed can better filter noise data, providing an important domain reference for starting field of federated learning in loV.
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- Type
- peer-review
- Language
- en
- Landing Page
- https://doi.org/10.7287/peerj-cs.1101v0.1/reviews/2
- OA Status
- gold
- References
- 39
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4297919655
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4297919655Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.7287/peerj-cs.1101v0.1/reviews/2Digital Object Identifier
- Title
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Peer Review #2 of "OES-Fed: a federated learning framework in vehicular network based on noise data filtering (v0.1)"Work title
- Type
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peer-reviewOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-09-20Full publication date if available
- Authors
-
Lei Yuan, Shir Li, Wang Corresp, Caiyu Su, Theam Foo Ng, Shir Li WangList of authors in order
- Landing page
-
https://doi.org/10.7287/peerj-cs.1101v0.1/reviews/2Publisher landing page
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://doi.org/10.7287/peerj-cs.1101v0.1/reviews/2Direct OA link when available
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Computer science, Noise (video), Artificial intelligence, Image (mathematics)Top concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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39Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.sufficient | 49 |
| abstract_inverted_index.Exponential | 90 |
| abstract_inverted_index.exponential | 134 |
| abstract_inverted_index.implemented | 125 |
| abstract_inverted_index.information | 14 |
| abstract_inverted_index.intelligent | 9, 12, 17 |
| abstract_inverted_index.interactive | 6 |
| abstract_inverted_index.management, | 11 |
| abstract_inverted_index.obscuration | 79 |
| abstract_inverted_index.perspective | 117 |
| abstract_inverted_index.experimental | 137 |
| abstract_inverted_index.vehicles.One | 23 |
| abstract_inverted_index.communication | 60 |
| abstract_inverted_index.problems.More | 99 |
| abstract_inverted_index.specifically, | 100 |
| abstract_inverted_index.algorithms.The | 136 |
| abstract_inverted_index.models.Besides, | 59 |
| abstract_inverted_index.perspective.The | 120 |
| abstract_inverted_index.cameras.Therefore | 82 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5059367213 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 6 |
| corresponding_institution_ids | https://openalex.org/I143923087 |
| citation_normalized_percentile |