Federated Transfer–Ordered–Personalized Learning for Driver Monitoring Application

Yuan, Liangqi; Su, Lu; Wang, Ziran

doi:10.1109/jiot.2023.3279273

Cited by 8 publications

(2 citation statements)

References 34 publications

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“…Furthermore, CAVs could also provide enhanced mobility services to users, such as adaptive routing and ride-sharing. Notably, the combination of digital twins and federated learning has demonstrated its potential in efficiently powering the development of CAVs efficiently as demonstrated in [231], [232]. on various factors, such as dynamics, driving behavior, road conditions, and traffic flow.…”

Section: A Enabling Techniquesmentioning

confidence: 99%

A Systematic Survey of Control Techniques and Applications in Connected and Automated Vehicles

Liu,

Hua,

Deng

et al. 2023

IEEE Internet Things J.

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Vehicle control is one of the most critical challenges in autonomous vehicles (AVs) and connected and automated vehicles (CAVs), and it is paramount in vehicle safety, passenger comfort, transportation efficiency, and energy saving. This survey attempts to provide a comprehensive and thorough overview of the current state of vehicle control technology, focusing on the evolution from vehicle state estimation and trajectory tracking control in AVs at the microscopic level to collaborative control in CAVs at the macroscopic level. First, this review starts with vehicle key state estimation, specifically vehicle sideslip angle, which is the most pivotal state for vehicle trajectory control, to discuss representative approaches. Then, we present symbolic vehicle trajectory tracking control approaches for AVs. On top of that, we further review the collaborative control frameworks for CAVs and corresponding applications. Finally, this survey concludes with a discussion of future research directions and the challenges. This survey aims to provide a contextualized and indepth look at the state of the art in vehicle control for AVs and CAVs, identifying critical areas of focus and pointing out the potential areas for further exploration.

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Section: A Enabling Techniquesmentioning

confidence: 99%

A Systematic Survey of Control Techniques and Applications in Connected and Automated Vehicles

Liu,

Hua,

Deng

et al. 2023

IEEE Internet Things J.

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show abstract

“…FedSup employs Bayesian Convolutional Neural Networks for fatigue detection in the Internet of Vehicles, showcasing reduced communication costs and improved training [38]. Federated Transfer-Ordered-Personalized Learning (FedTOP) is tailored for driver monitoring, demonstrating improved accuracy, efficiency, and scalability across two real-world datasets [39,40]. A Hybrid Federated and Centralized Learning (HFCL) framework merges the advantages of federated and centralized learning, achieving up to 20% higher accuracy and 50% less communication overhead [41].…”

Section: Related Workmentioning

confidence: 99%

An Improved Big Data Analytics Architecture Using Federated Learning for IoT-Enabled Urban Intelligent Transportation Systems

Kaleem,

Sohail,

Tariq

et al. 2023

Sustainability

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The exponential growth of the Internet of Things has precipitated a revolution in Intelligent Transportation Systems, notably in urban environments. An ITS leverages advancements in communication technologies and data analytics to enhance the efficiency and intelligence of transport networks. At the same time, these IoT-enabled ITSs generate a vast array of complex data classified as Big Data. Traditional data analytics frameworks need help to efficiently process these Big Data due to its sheer volume, velocity, variety, and significant data privacy concerns. Federated Learning, known for its privacy-preserving attributes, is a promising technology for implementation within ITSs for IoT-generated Big Data. Nevertheless, the system faces challenges due to the variable nature of devices, the heterogeneity of data, and the dynamic conditions in which ITS operates. Recent efforts to mitigate these challenges focus on the practical selection of an averaging mechanism during the server’s aggregation phase and practical dynamic client training. Despite these efforts, existing research still relies on personalized FL with personalized averaging and client training. This paper presents a personalized architecture, including an optimized Federated Averaging strategy that leverages FL for efficient and real-time Big Data analytics in IoT-enabled ITSs. Various personalization methods are applied to enhance the traditional averaging algorithm. Local fine-tuning and weighted averaging tailor the global model to individual client data. Custom learning rates are utilized to boost the performance further. Regular evaluations are advised to maintain model efficacy. The proposed architecture addresses critical challenges like real-life federated environment settings, data integration, and significant data privacy, offering a comprehensive solution for modern urban transportation systems using Big Data. Using the Udacity Self-Driving Car Dataset foe vehicle detection, we apply the proposed approaches to demonstrate the efficacy of our model. Our empirical findings validate the superiority of our architecture in terms of scalability, real-time decision-making capabilities, and data privacy preservation. We attained accuracy levels of 93.27%, 92.89%, and 92.96% for our proposed model in a Federated Learning architecture with 10 nodes, 20 nodes, and 30 nodes, respectively.

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Rethinking privacy in the Internet of Things: a comprehensive review of consumer studies and theories

Tudoran

2024

INTR

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PurposeThis study aims to analyze and synthesize literature on consumer privacy-related behavior and intelligent device-to-device interactions within the Internet of Things (IoT).Design/methodology/approachWe conducted a systematic review using Elsevier’s Scopus database, focusing on studies published in English from 2000 to 2023. The review targeted articles within selected social sciences and business disciplines, specifically concerning consumer behavior in IoT contexts.FindingsWe categorized the privacy literature into three thematic clusters: legislation and policy, business implications and consumer behavior. Within the consumer behavior cluster, our analysis indicates a shift from general Internet and e-commerce privacy concerns prior to 2016, toward issues related to advertising and policy between 2017 and 2018, and increasingly toward pronounced concerns in technological systems, particularly IoT, from 2019 onwards. We identify eight distinct areas of privacy concern within IoT and propose a framework that links antecedents and privacy concerns to subsequent attitudes and behaviors. This framework highlights varying patterns of information disclosure and bridges theoretical constructs with empirical research in IoT privacy.Originality/valueOriginality lies in enhancing the Antecedents-Privacy Concerns-Outcomes (APCO) macro-model by integrating diverse theoretical perspectives on technological and individual-specific antecedents, alongside privacy concerns and beliefs. This comprehensive integration enriches the framework, enabling it to predict and categorize consumer behavior in IoT environments more effectively. The revised model provides a robust tool for understanding privacy-related behavior within the IoT, significantly enriching its theoretical relevance and practical applicability.

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Federated Transfer–Ordered–Personalized Learning for Driver Monitoring Application

Cited by 8 publications

References 34 publications

A Systematic Survey of Control Techniques and Applications in Connected and Automated Vehicles

A Systematic Survey of Control Techniques and Applications in Connected and Automated Vehicles

An Improved Big Data Analytics Architecture Using Federated Learning for IoT-Enabled Urban Intelligent Transportation Systems

Rethinking privacy in the Internet of Things: a comprehensive review of consumer studies and theories

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