Content-based Vehicle Selection and Resource Allocation for Federated Learning in IoV

Wang, Siyu; Liu, Fangfang; Xia, Hailun

doi:10.1109/wcncw49093.2021.9419986

Cited by 36 publications

(12 citation statements)

References 16 publications

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“…Currently, there is no popular mechanism that can select non-redundant data from CAVs to minimize the network strain. There are ongoing efforts to develop robust methods to select vehicles and resource allocation schemes [73]- [75]. In [76], the overall training process was demonstrated to be efficient due to incorporating a client selection model.…”

Section: B Imperfect Methodologymentioning

confidence: 99%

A Survey of Federated Learning for Connected and Automated Vehicles

Chellapandi¹,

Yuan²,

Zak³

et al. 2023

Preprint

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Connected and Automated Vehicles (CAVs) are one of the emerging technologies in the automotive domain that has the potential to alleviate the issues of accidents, traffic congestion, and pollutant emissions, leading to a safe, efficient, and sustainable transportation system. Machine learningbased methods are widely used in CAVs for crucial tasks like perception, motion planning, and motion control, where machine learning models in CAVs are solely trained using the local vehicle data, and the performance is not certain when exposed to new environments or unseen conditions. Federated learning (FL) is an effective solution for CAVs that enables a collaborative model development with multiple vehicles in a distributed learning framework. FL enables CAVs to learn from a wide range of driving environments and improve their overall performance while ensuring the privacy and security of local vehicle data. In this paper, we review the progress accomplished by researchers in applying FL to CAVs. A broader view of the various data modalities and algorithms that have been implemented on CAVs is provided. Specific applications of FL are reviewed in detail, and an analysis of the challenges and future scope of research are presented.

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Section: B Imperfect Methodologymentioning

confidence: 99%

A Survey of Federated Learning for Connected and Automated Vehicles

Chellapandi¹,

Yuan²,

Zak³

et al. 2023

Preprint

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show abstract

“…In [114], the authors take image classification as the scene, comprehensively consider loss function decay, wireless resources, computing resources, and energy as selection indicators for the first time, take learning efficiency as the final optimization goal, obtain vehicle selection and resource allocation schemes based on data content. The work in [115] goes a step further, utilizing a fuzzy logic algorithm based on stability factor (vehicles with a relatively low speed can always be selected), topology factor, and connection factor (to ensure communication quality), considering vehicle speed, vehicle distribution and wireless link connections between vehicles, to select the appropriate edge vehicles for RSUs communication.…”

Section: Generated Dataset 2021mentioning

confidence: 99%

“…Meanwhile, there is a lack of performance testing and experimental verification in real scenarios, i.e., there are very few works to test their defense models with distinct attack types, including backdoor attacks, symbol flipping attacks, etc. Participant Selection [115] Fuzzy logic algorithm A trade-off between accuracy and communication overhead 2021 [114] Genetic algorithm More accurate, converge faster 2021 [116] Selection frequency Model accuracy increases 20% 2022 [117] Convergence proof, less communication overhead 2020 [118] Two-dimension contract theory, greedy algorithm…”

Section: High Information Sensitivitymentioning

confidence: 99%

Enhanced Federated Learning for Edge Data Security in Intelligent Transportation Systems

Zhu

Yin

et al. 2023

IEEE Trans. Intell. Transport. Syst.

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“…It embodies the notion that hardware and software characteristics can be measured, improved and perhaps guaranteed. Among the existing researches of QoE, it is proposed that in the multi-user condition every edge cell will firstly allocate the network resource among its users according to a specific rule, then users that can no longer satisfied by their related edge cell will be gathered, who will be jointly served by all the edge cells with abundant resource [24][25][26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Digital Twin Power Grid Oriented Mobile Edge Network Resource Allocation Model

Han,

Chen,

et al. 2023

IEEJ Transactions Elec Engng

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The integration of vast quantities of clean energy causes intense impact on the power grid reliability. Digital Twin can be used to handle the high‐growth data flow generated in the power grid, and thus accelerate its digitalization process to fulfill the clean energy consumption target. As the traditional central cloud network structure can no longer effectively support the operation of digital twin power grid, the mobile edge network can be used as a reasonable substitute. To support the information interaction process of the mobile edge network, considering total profit, time delay and energy consumption, this paper proposes the mobile edge network communication and computing resource joint allocation model. On the basis, the GA‐PSO algorithm is proposed to solve the resource allocation optimization function. The feasibility and effectiveness of the proposed model is verified by the related simulation experiment. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

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Content-based Vehicle Selection and Resource Allocation for Federated Learning in IoV

Cited by 36 publications

References 16 publications

A Survey of Federated Learning for Connected and Automated Vehicles

A Survey of Federated Learning for Connected and Automated Vehicles

Enhanced Federated Learning for Edge Data Security in Intelligent Transportation Systems

Digital Twin Power Grid Oriented Mobile Edge Network Resource Allocation Model

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