DAG-based swarm learning: A secure asynchronous learning framework for Internet of Vehicles
To provide diversified services in the intelligent transportation systems, smart vehicles will generate unprecedented amounts of data every day. Due to data security and user privacy issues, Federated Learning (FL) is considered a potential solution to ensure privacy-preserving in data sharing. Howe...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2024-12-01
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| Series: | Digital Communications and Networks |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352864823001578 |
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| _version_ | 1846101670320144384 |
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| author | Xiaoge Huang Hongbo Yin Qianbin Chen Yu Zeng Jianfeng Yao |
| author_facet | Xiaoge Huang Hongbo Yin Qianbin Chen Yu Zeng Jianfeng Yao |
| author_sort | Xiaoge Huang |
| collection | DOAJ |
| description | To provide diversified services in the intelligent transportation systems, smart vehicles will generate unprecedented amounts of data every day. Due to data security and user privacy issues, Federated Learning (FL) is considered a potential solution to ensure privacy-preserving in data sharing. However, there are still many challenges to applying the traditional synchronous FL directly in the Internet of Vehicles (IoV), such as unreliable communications and malicious attacks. In this paper, we propose a Directed Acyclic Graph (DAG) based Swarm Learning (DSL), which integrates edge computing, FL, and blockchain technologies to provide secure data sharing and model training in IoVs. To deal with the high mobility of vehicles, the dynamic vehicle association algorithm is introduced, which could optimize the connections between vehicles and road side units to improve the training efficiency. Moreover, to enhance the anti-attack property of the DSL algorithm, a malicious attack detection method is adopted, which could recognize malicious vehicles by the site confirmation rate. Furthermore, an accuracy-based reward mechanism is developed to promote vehicles to participate in the model training with honest behaviors. Finally, simulation results demonstrate that the proposed DSL algorithm could achieve better performance in terms of model accuracy, convergence rates and security compared with existing algorithms. |
| format | Article |
| id | doaj-art-f8bcdc9f4dda4809a43392fdebbcc7b2 |
| institution | Kabale University |
| issn | 2352-8648 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Digital Communications and Networks |
| spelling | doaj-art-f8bcdc9f4dda4809a43392fdebbcc7b22024-12-29T04:47:34ZengKeAi Communications Co., Ltd.Digital Communications and Networks2352-86482024-12-0110616111621DAG-based swarm learning: A secure asynchronous learning framework for Internet of VehiclesXiaoge Huang0Hongbo Yin1Qianbin Chen2Yu Zeng3Jianfeng Yao4School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China; Corresponding author.School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, 400065, ChinaSchool of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, 400065, ChinaShanghai Cygnus Semiconductor Co., Ltd., Shanghai, ChinaChongqing Changan Automobile Co., Ltd., Chongqing, ChinaTo provide diversified services in the intelligent transportation systems, smart vehicles will generate unprecedented amounts of data every day. Due to data security and user privacy issues, Federated Learning (FL) is considered a potential solution to ensure privacy-preserving in data sharing. However, there are still many challenges to applying the traditional synchronous FL directly in the Internet of Vehicles (IoV), such as unreliable communications and malicious attacks. In this paper, we propose a Directed Acyclic Graph (DAG) based Swarm Learning (DSL), which integrates edge computing, FL, and blockchain technologies to provide secure data sharing and model training in IoVs. To deal with the high mobility of vehicles, the dynamic vehicle association algorithm is introduced, which could optimize the connections between vehicles and road side units to improve the training efficiency. Moreover, to enhance the anti-attack property of the DSL algorithm, a malicious attack detection method is adopted, which could recognize malicious vehicles by the site confirmation rate. Furthermore, an accuracy-based reward mechanism is developed to promote vehicles to participate in the model training with honest behaviors. Finally, simulation results demonstrate that the proposed DSL algorithm could achieve better performance in terms of model accuracy, convergence rates and security compared with existing algorithms.http://www.sciencedirect.com/science/article/pii/S2352864823001578Direct acyclic graphInternet of VehiclesSwarm learningAsynchronous learning |
| spellingShingle | Xiaoge Huang Hongbo Yin Qianbin Chen Yu Zeng Jianfeng Yao DAG-based swarm learning: A secure asynchronous learning framework for Internet of Vehicles Digital Communications and Networks Direct acyclic graph Internet of Vehicles Swarm learning Asynchronous learning |
| title | DAG-based swarm learning: A secure asynchronous learning framework for Internet of Vehicles |
| title_full | DAG-based swarm learning: A secure asynchronous learning framework for Internet of Vehicles |
| title_fullStr | DAG-based swarm learning: A secure asynchronous learning framework for Internet of Vehicles |
| title_full_unstemmed | DAG-based swarm learning: A secure asynchronous learning framework for Internet of Vehicles |
| title_short | DAG-based swarm learning: A secure asynchronous learning framework for Internet of Vehicles |
| title_sort | dag based swarm learning a secure asynchronous learning framework for internet of vehicles |
| topic | Direct acyclic graph Internet of Vehicles Swarm learning Asynchronous learning |
| url | http://www.sciencedirect.com/science/article/pii/S2352864823001578 |
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