國立虎尾科技大學 |

Simulation of Autonomous Mobile Vehicles Using Federated Learning.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Simulation of Autonomous Mobile Vehicles Using Federated Learning./
Author:	Paloju, Sathish Kumar Chary.
Description:	1 online resource (60 pages)
Notes:	Source: Masters Abstracts International, Volume: 85-12.
Contained By:	Masters Abstracts International85-12.
Subject:	Computer science. -
Online resource:	click for full text (PQDT)
ISBN:	9798383058640

Simulation of Autonomous Mobile Vehicles Using Federated Learning.
Paloju, Sathish Kumar Chary.

Simulation of Autonomous Mobile Vehicles Using Federated Learning. - 1 online resource (60 pages)

Source: Masters Abstracts International, Volume: 85-12.

Thesis (M.S.)--Southern Illinois University at Carbondale, 2024.

Includes bibliographical references

Federated learning(FL), a form of distributed machine learning, facilitates collaboration among multiple devices, such as autonomous vehicles in this context, aiming to enhance object detection accuracy without compromising individual data privacy. This approach allows vehicles to refine their models without divulging sensitive information like location, routes, or personal preferences. Training data remains on each device in FL, enabling model updates based on local data without transmitting raw data to a central repository. The refined models are aggregated on a central server, making an improved global model accessible to all participating devices. The BDDK100 dataset, comprising over 100,000 high-resolution images from video recordings captured by a front-facing camera on a moving vehicle, serves as a benchmark dataset for autonomous driving. This simulation study employs three simulated clients, each representing a distinct autonomous vehicle, engaged in collaborative training of a deep neural network for object detection through the FL paradigm. The YOLOV5 model is used as object detection model. In conclusion, this study demonstrates the efficacy of FL in the context of autonomous vehicle systems, specifically in object detection. The collaborative training approach, wherein autonomous vehicles collectively refine a deep neural network without compromising data privacy, holds promise for enhancing the overall accuracy and performance of object detection models. The utilization of the BDDK100 dataset underscores the practical applicability of the FL methodology in real-world scenarios, establishing it as a viable solution for improving machine learning models in decentralized environments. As autonomous technologies evolve, FL stands out as a privacy-preserving and collaborative method, contributing to advancing intelligent systems without sacrificing individual data security.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798383058640Subjects--Topical Terms:

573171
Computer science.
Subjects--Index Terms:

Data securityIndex Terms--Genre/Form:

554714
Electronic books.

Simulation of Autonomous Mobile Vehicles Using Federated Learning.
LDR:03264ntm a22003977 4500 001 1150173
005 20241022111606.5
006 m o d
007 cr bn ---uuuuu
008 250605s2024 xx obm 000 0 eng d
020 $a 9798383058640
035 $a (MiAaPQ)AAI31146042
035 $a AAI31146042
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Paloju, Sathish Kumar Chary. $3 1476608
245 1 0 $a Simulation of Autonomous Mobile Vehicles Using Federated Learning.
264 0 $c 2024
300 $a 1 online resource (60 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Masters Abstracts International, Volume: 85-12.
500 $a Advisor: Khaled, Ahmed R.
502 $a Thesis (M.S.)--Southern Illinois University at Carbondale, 2024.
504 $a Includes bibliographical references
520 $a Federated learning(FL), a form of distributed machine learning, facilitates collaboration among multiple devices, such as autonomous vehicles in this context, aiming to enhance object detection accuracy without compromising individual data privacy. This approach allows vehicles to refine their models without divulging sensitive information like location, routes, or personal preferences. Training data remains on each device in FL, enabling model updates based on local data without transmitting raw data to a central repository. The refined models are aggregated on a central server, making an improved global model accessible to all participating devices. The BDDK100 dataset, comprising over 100,000 high-resolution images from video recordings captured by a front-facing camera on a moving vehicle, serves as a benchmark dataset for autonomous driving. This simulation study employs three simulated clients, each representing a distinct autonomous vehicle, engaged in collaborative training of a deep neural network for object detection through the FL paradigm. The YOLOV5 model is used as object detection model. In conclusion, this study demonstrates the efficacy of FL in the context of autonomous vehicle systems, specifically in object detection. The collaborative training approach, wherein autonomous vehicles collectively refine a deep neural network without compromising data privacy, holds promise for enhancing the overall accuracy and performance of object detection models. The utilization of the BDDK100 dataset underscores the practical applicability of the FL methodology in real-world scenarios, establishing it as a viable solution for improving machine learning models in decentralized environments. As autonomous technologies evolve, FL stands out as a privacy-preserving and collaborative method, contributing to advancing intelligent systems without sacrificing individual data security.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Computer science. $3 573171
650 4 $a Engineering. $3 561152
650 4 $a Information technology. $3 559429
653 $a Data security
653 $a Federated learning
653 $a Machine learning
653 $a Autonomous vehicles
655 7 $a Electronic books. $2 local $3 554714
690 $a 0984
690 $a 0489
690 $a 0800
690 $a 0537
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Southern Illinois University at Carbondale. $b Computer Science. $3 1190734
773 0 $t Masters Abstracts International $g 85-12.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31146042 $z click for full text (PQDT)