Federated Learning for Computational Offloading and Resource Management of Vehicular Edge Computing in 6G-V2X Network

Hasan, Mohammad Kamrul; Jahan, Nusrat; Nazri, Mohd Zakree Ahmad; Islam, Shayla; Attique Khan, Muhammad; Alzahrani, Ahmed Ibrahim; Alalwan, Nasser; Nam, Yunyoung

doi:10.1109/tce.2024.3357530

Cited by 6 publications

(1 citation statement)

References 95 publications

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“…This involves exploring novel edge computing frameworks, algorithms, and architectures that optimize the distribution of computational tasks across edge devices, fog nodes, and cloud servers. Advanced edge computing paradigms such as federated learning [109], distributed learning [110], and multi-agent reinforcement learning [111] are being investigated to enhance efficiency and effectiveness in edge computing environments. These paradigms enable collaborative learning across multiple devices without the need to centralize data, thus preserving privacy and reducing bandwidth consumption.…”

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confidence: 99%

A Survey of Machine Learning in Edge Computing: Techniques, Frameworks, Applications, Issues, and Research Directions

Jouini,

Sethom,

Namoun

et al. 2024

Technologies

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Internet of Things (IoT) devices often operate with limited resources while interacting with users and their environment, generating a wealth of data. Machine learning models interpret such sensor data, enabling accurate predictions and informed decisions. However, the sheer volume of data from billions of devices can overwhelm networks, making traditional cloud data processing inefficient for IoT applications. This paper presents a comprehensive survey of recent advances in models, architectures, hardware, and design requirements for deploying machine learning on low-resource devices at the edge and in cloud networks. Prominent IoT devices tailored to integrate edge intelligence include Raspberry Pi, NVIDIA’s Jetson, Arduino Nano 33 BLE Sense, STM32 Microcontrollers, SparkFun Edge, Google Coral Dev Board, and Beaglebone AI. These devices are boosted with custom AI frameworks, such as TensorFlow Lite, OpenEI, Core ML, Caffe2, and MXNet, to empower ML and DL tasks (e.g., object detection and gesture recognition). Both traditional machine learning (e.g., random forest, logistic regression) and deep learning methods (e.g., ResNet-50, YOLOv4, LSTM) are deployed on devices, distributed edge, and distributed cloud computing. Moreover, we analyzed 1000 recent publications on “ML in IoT” from IEEE Xplore using support vector machine, random forest, and decision tree classifiers to identify emerging topics and application domains. Hot topics included big data, cloud, edge, multimedia, security, privacy, QoS, and activity recognition, while critical domains included industry, healthcare, agriculture, transportation, smart homes and cities, and assisted living. The major challenges hindering the implementation of edge machine learning include encrypting sensitive user data for security and privacy on edge devices, efficiently managing resources of edge nodes through distributed learning architectures, and balancing the energy limitations of edge devices and the energy demands of machine learning.

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confidence: 99%

A Survey of Machine Learning in Edge Computing: Techniques, Frameworks, Applications, Issues, and Research Directions

Jouini,

Sethom,

Namoun

et al. 2024

Technologies

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Federated learning enables 6 G communication technology: Requirements, applications, and integrated with intelligence framework

Hasan,

Habib,

Islam

et al. 2024

Alexandria Engineering Journal

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Enhancing the Safety of Autonomous Vehicles: Semi-Supervised Anomaly Detection With Overhead Fisheye Perspective

Tsiktsiris,

Lalas,

Dasygenis

et al. 2024

IEEE Access

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Autonomous vehicles (AVs) have the potential to revolutionize transportation. However, ensuring passenger safety within these vehicles in the absence of a dedicated onboard authority figure necessitates the development of intelligent, autonomous surveillance systems. This paper presents a novel semi-supervised anomaly detection system specifically designed to enhance safety within autonomous shuttles. Our approach leverages overhead fisheye cameras to provide comprehensive, occlusion-resistant monitoring of the cabin interior. This unique perspective maximizes visibility, even in crowded conditions. Our spatiotemporal autoencoder architecture, composed of both convolutional and reccurrent layers, is trained on extensive unlabeled video data to learn representations of regular passenger behavior using a Center-Weighted Loss (CWL) function, which focuses in the cabin's central region where critical events are most likely to occur. This reduces the potential for false positives triggered by rapid changes on the periphery due to the vehicle's movement. To enhance the system's ability to discriminate between specific safety and security incidents, we introduce a classifier fine-tuned on a labeled subset of our dataset. We evaluate our method's performance through experimentation on a real-world dataset (CERTH-AV) collected with an overhead fisheye camera. Our method demonstrates superior anomaly detection capabilities, achieving the highest Area Under the Curve (AUC) performance on the CERTH-AV dataset. Further comparative evaluations on established benchmarks, including UCF-Crime and ShanghaiTech, validate our system's robustness and adaptability. Finally, we have successfully integrated our method into autonomous minibuses using NVIDIA Jetson embedded systems for real-time processing, demonstrating the practical efficacy of our approach in safeguarding passengers within autonomous vehicles.INDEX TERMS Anomaly detection, artificial intelligence (AI), autonomous vehicles (AVs), computer vision, edge computing, overhead fisheye imaging

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Federated Learning for Computational Offloading and Resource Management of Vehicular Edge Computing in 6G-V2X Network

Cited by 6 publications

References 95 publications

A Survey of Machine Learning in Edge Computing: Techniques, Frameworks, Applications, Issues, and Research Directions

A Survey of Machine Learning in Edge Computing: Techniques, Frameworks, Applications, Issues, and Research Directions

Federated learning enables 6 G communication technology: Requirements, applications, and integrated with intelligence framework

Enhancing the Safety of Autonomous Vehicles: Semi-Supervised Anomaly Detection With Overhead Fisheye Perspective

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Federated Learning for Computational Offloading and Resource Management of Vehicular Edge Computing in 6G-V2X Network

Cited by 6 publications

References 95 publications

A Survey of Machine Learning in Edge Computing: Techniques, Frameworks, Applications, Issues, and Research Directions

A Survey of Machine Learning in Edge Computing: Techniques, Frameworks, Applications, Issues, and Research Directions

Federated learning enables 6 G communication technology: Requirements, applications, and integrated with intelligence framework

Enhancing the Safety of Autonomous Vehicles: Semi-Supervised Anomaly Detection With Overhead Fisheye Perspective

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Product

Resources

About

Federated learning enables 6 G communication technology: Requirements, applications, and integrated with intelligence framework