An optimized federated learning framework for internet of vehicles based on noise data and incremental data processing

Bibliographic Details
Main Author:	Lei, Yuan
Format:	Thesis
Language:	English
Published:	2024
Subjects:	QA Mathematics
Online Access:	https://ir.upsi.edu.my/detailsg.php?det=12869
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author	Lei, Yuan
author_facet	Lei, Yuan
author_sort	Lei, Yuan
description
format	Thesis
id	upsi-12869
institution	Universiti Pendidikan Sultan Idris
language	English
publishDate	2024
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spelling	upsi-128692025-06-26 An optimized federated learning framework for internet of vehicles based on noise data and incremental data processing 2024 Lei, Yuan QA Mathematics <p>Internet of Vehicles (IoV) technology has been rapidly advancing, making intelligent transportation systems the future trend. This research revolves around building efficient and secure vehicular networks using data processing mechanisms backed by machine learning and information security. However, noise and incremental data present challenges to vehicular network development. This study proposes two novel federated learning frameworks, namely the Outlier Detection and Exponential Smoothing Federated Learning (OES-FED) and Federated Learning Framework Based on Incremental Weighting and Diversity Selection for IoV (FED-IW&DS), to overcome the above problems. The OES-FED framework leveraged anomaly detection and exponential smoothing to filter noise data, thus, improving model robustness and enhancing communication efficiency. In terms of accuracy, it outperformed the existing Federated Learning-Average (FED-AVG) and FED-SGD models on three datasets by 44.46% and 2.36%, respectively. Furthermore, the FED-IW&DS framework that integrates incremental weights and diversity selection to effectively deal with issues of growing data scale was able to achieve rapid information sharing while preserving user privacy. The superiority of FED-IW&DS was clearly proven through its performance on two data sets, which found its accuracy to exceed that of the Fed-prox model by 30- 35%. Ultimately, integrating the OES-FED and FED-IW&DS frameworks unveiled two critical integration points: the execution order and transition point of the two frameworks. By synergistically integrating the two frameworks, the proposed strategy unlocked new federated learning solutions for IoV as it yielded up to 5-10% higher accuracy compared to employing either framework individually. This study highlights novel approaches that address noise and incremental data challenges in IoV, yielding substantial advancements in both theoretical research and practical applications. The research outcomes have several implications, of which the proposed solutions play an essential role in improving communication efficiency, enhancing data processing capabilities, protecting user privacy, and providing crucial theoretical support and practical reference for future research and optimization of data processing mechanisms in IoV.</p> 2024 thesis https://ir.upsi.edu.my/detailsg.php?det=12869 https://ir.upsi.edu.my/detailsg.php?det=12869 text eng N/A openAccess Doctoral Perpustakaan Tuanku Bainun Fakulti Komputeran dan META-Teknologi <p>Acheampong, R. 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spellingShingle	QA Mathematics Lei, Yuan An optimized federated learning framework for internet of vehicles based on noise data and incremental data processing
thesis_level	PhD
title	An optimized federated learning framework for internet of vehicles based on noise data and incremental data processing
title_full	An optimized federated learning framework for internet of vehicles based on noise data and incremental data processing
title_fullStr	An optimized federated learning framework for internet of vehicles based on noise data and incremental data processing
title_full_unstemmed	An optimized federated learning framework for internet of vehicles based on noise data and incremental data processing
title_short	An optimized federated learning framework for internet of vehicles based on noise data and incremental data processing
title_sort	optimized federated learning framework for internet of vehicles based on noise data and incremental data processing
topic	QA Mathematics
url	https://ir.upsi.edu.my/detailsg.php?det=12869
work_keys_str_mv	AT leiyuan anoptimizedfederatedlearningframeworkforinternetofvehiclesbasedonnoisedataandincrementaldataprocessing AT leiyuan optimizedfederatedlearningframeworkforinternetofvehiclesbasedonnoisedataandincrementaldataprocessing

An optimized federated learning framework for internet of vehicles based on noise data and incremental data processing

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