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Federated Learning for Connected and...

Chellapandi, Vishnu Pandi,

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Federated Learning for Connected and Automated Vehicles /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Federated Learning for Connected and Automated Vehicles // Vishnu Pandi Chellapandi.
作者:	Chellapandi, Vishnu Pandi,
面頁冊數:	1 electronic resource (166 pages)
附註:	Source: Dissertations Abstracts International, Volume: 86-09, Section: B.
Contained By:	Dissertations Abstracts International86-09B.
標題:	Privacy. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31882424
ISBN:	9798310117624

Federated Learning for Connected and Automated Vehicles /
Chellapandi, Vishnu Pandi,

Federated Learning for Connected and Automated Vehicles /Vishnu Pandi Chellapandi. - 1 electronic resource (166 pages)

Source: Dissertations Abstracts International, Volume: 86-09, Section: B.

The subject of this dissertation is the development of Machine Learning (ML) algorithms and their applications in Connected and Automated Vehicles (CAV). Decentralized machine learning algorithms have been proposed for CAV with noisy communication channels. Decentralized ML algorithms, referred to as Federated Learning (FL), are developed for multiple vehicles to collaboratively train models, thus enhancing performance while ensuring data privacy and security. Applications of FL for CAV (FL4CAV) are analyzed. Both centralized and decentralized FL frameworks are considered, along with various data sources, models, and data security techniques relevant to FL in CAVs. Three innovative algorithms for Decentralized Federated Learning (DFL) that effectively handle noisy communication channels are proposed. Theoretical and experimental results demonstrate that the proposed algorithms that share gradients through noisy channels instead of parameters are more robust under noisy conditions compared to parameter-mixing algorithms. Building on the exploration of decentralized federated learning, a novel decentralized noisy model update tracking algorithm is proposed to further enhance robustness and efficiency while addressing the challenge of data heterogeneity impact. The proposed algorithm performs better than the existing algorithms in handling imperfect information sharing. Expanding on these findings, applications of FL are proposed for heavy-duty diesel engines, which remain crucial due to their fuel efficiency and emissions characteristics. Finally, an FL algorithm to better predict and control aftertreatment temperature overshoots in real-time is demonstrated.

English

ISBN: 9798310117624Subjects--Topical Terms:

528582
Privacy.
Subjects--Index Terms:

Machine Learning

Federated Learning for Connected and Automated Vehicles /
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520 $a The subject of this dissertation is the development of Machine Learning (ML) algorithms and their applications in Connected and Automated Vehicles (CAV). Decentralized machine learning algorithms have been proposed for CAV with noisy communication channels. Decentralized ML algorithms, referred to as Federated Learning (FL), are developed for multiple vehicles to collaboratively train models, thus enhancing performance while ensuring data privacy and security. Applications of FL for CAV (FL4CAV) are analyzed. Both centralized and decentralized FL frameworks are considered, along with various data sources, models, and data security techniques relevant to FL in CAVs. Three innovative algorithms for Decentralized Federated Learning (DFL) that effectively handle noisy communication channels are proposed. Theoretical and experimental results demonstrate that the proposed algorithms that share gradients through noisy channels instead of parameters are more robust under noisy conditions compared to parameter-mixing algorithms. Building on the exploration of decentralized federated learning, a novel decentralized noisy model update tracking algorithm is proposed to further enhance robustness and efficiency while addressing the challenge of data heterogeneity impact. The proposed algorithm performs better than the existing algorithms in handling imperfect information sharing. Expanding on these findings, applications of FL are proposed for heavy-duty diesel engines, which remain crucial due to their fuel efficiency and emissions characteristics. Finally, an FL algorithm to better predict and control aftertreatment temperature overshoots in real-time is demonstrated.
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590 $a School code: 0183
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650 4 $a Decision making. $3 517204
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650 4 $a Electrical engineering. $3 649834
653 $a Machine Learning
653 $a Federated Learning
653 $a Connected and Automated Vehicles
690 $a 0544
690 $a 0464
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