Blockchain-Empowered Decentralized Horizontal Federated Learning for 5G-Enabled UAVs

Feng, Chunyan; Bin, Liu; Yu, Keping; Goudos, Sotirios K.; Wan, Shaohua

doi:10.1109/tii.2021.3116132

Cited by 132 publications

(45 citation statements)

References 22 publications

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“…Recently, the Blockchain and ML synergy was also envisioned [61]- [63], where the Blockchain facilitates the verification of the training processes of ML, while reducing the risk of failures. Also, the Blockchain network can be used to store the training data sets that are used by the learning models to eliminate or minimize the data faults and errors.…”

Section: B the Emergence Of ML And Blockchainmentioning

confidence: 99%

Secure UAV-Aided Mobile Edge Computing for IoT: A Review

et al. 2022

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As the Internet of Things (IoT) ecosystem evolves, innovative applications with stringent demands with respect to latency will emerge. To handle computation-intensive tasks in a timely manner, data offloading to Mobile Edge Computing (MEC) servers has been suggested. On the other hand, prospective IoT networks are expected to include Unmanned Aerial Vehicles (UAVs) to enhance coverage and connectivity, while retaining reliable communication links with ground nodes in urban, suburban, and rural terrain. Nevertheless, the evolution of UAV-aided MEC-enabled IoT presupposes the mitigation of security threats through the implementation of efficient and robust countermeasures. As UAVs inherently have certain limitations in terms of energy, computational, and memory resources, designing lightweight security solutions is required. This paper provides an overview of the UAV-aided MEC-enabled IoT and a detailed presentation of use cases and application scenarios, where security is of utmost importance. Subsequently, up-to-date research works on security solutions for the UAV-aided MEC-enabled IoT are comprehensively presented. To this end, the adoption of information-theoretic techniques that ensure adequate Physical-Layer Security (PLS) is discussed along with sophisticated security approaches based on emerging technologies, such as Blockchain and Machine Learning (ML). In addition, research studies on software-and hardware-based methods for the identification and authentication of network nodes are presented. Finally, this paper provides future perspectives in this research domain, stimulating further work.INDEX TERMS Blockchain, Internet of Things (IoT), Machine Learning (ML), Mobile Edge Computing (MEC), Physical-Layer Security (PLS), Unmanned Aerial Vehicle (UAV).

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Section: B the Emergence Of ML And Blockchainmentioning

confidence: 99%

Secure UAV-Aided Mobile Edge Computing for IoT: A Review

et al. 2022

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“…3) Excessive energy consumption will be generated, especially for image data transmission. Federated Learning (FL) [17] based on edge-cloud cooperation (E-CC) may be a new solution to solve the above problems, in which each terminal uses locally collected data to train the neural network at the edge, and only weights are uploaded back to the server. Since this strategy aims to solve isolated data islands problems and protect the privacy and data security, the edge layer needs to complete data labeling and dataset training locally.…”

Section: Introductionmentioning

confidence: 99%

Edge YOLO: Real-Time Intelligent Object Detection System Based on Edge-Cloud Cooperation in Autonomous Vehicles

Liang

Zhen

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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110

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Driven by the ever-increasing requirements of autonomous vehicles, such as traffic monitoring and driving assistant, deep learning-based object detection (DL-OD) has been increasingly attractive in intelligent transportation systems. However, it is difficult for the existing DL-OD schemes to realize the responsible, cost-saving, and energy-efficient autonomous vehicle systems due to low their inherent defects of low timeliness and high energy consumption. In this paper, we propose an object detection (OD) system based on edge-cloud cooperation and reconstructive convolutional neural networks, which is called Edge YOLO. This system can effectively avoid the excessive dependence on computing power and uneven distribution of cloud computing resources. Specifically, it is a lightweight OD framework realized by combining pruning feature extraction network and compression feature fusion network to enhance the efficiency of multi-scale prediction to the largest extent. In addition, we developed an autonomous driving platform equipped with NVIDIA Jetson for system-level verification. We experimentally demonstrate the reliability and efficiency of Edge YOLO on COCO2017 and KITTI data sets, respectively. According to COCO2017 standard datasets with a speed of 26.6 frames per second (FPS), the results show that the number of parameters in the entire network is only 25.67 MB, while the accuracy (mAP) is up to 47.3%.

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“…At present, digital archives of water conservancy projects have less relevant research in foreign countries, and the research in China is also in the initial stage [5,6]. Although the new "Archives Law of the People's Republic of China" provides legal and policy guarantees for the informationization of construction files of water conservancy project construction, the relevant research and application are still focused on the initial stage of construction [7].…”

Section: Introductionmentioning

confidence: 99%

A Data Management Model for Intelligent Water Project Construction Based on Blockchain

Wang

et al. 2022

Wireless Communications and Mobile Computing

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The engineering construction-related data is essential for evaluating and tracing project quality in industry 4.0. Specifically, the preservation of the information is of great significance to the safety of intelligent water projects. This paper proposes a blockchain-based data management model for intelligent water projects to achieve standardization management and long-term preservation of archives. Based on studying the concrete production process in water conservancy project construction, we first build a behavioral model and the corresponding role assignment strategy to describe the standardized production process. Then, a distributed blockchain data structure for storing the production-related files is designed according to the model and strategy. In addition, to provide trust repository and transfer on the construction data, an intelligent keyless signature based on edge computing is employed to manage the data’s entry, modification, and approval. Finally, standardized and secure information is uploaded onto the blockchain to supervise intelligent water project construction quality and safety effectively. The experiments showed that the proposed model reduced the time and labor cost when generating the production data and ensured the security and traceability of the electronic archiving of the documents. Blockchain and intelligent keyless signatures jointly provide new data sharing and trading methods in intelligent water systems.

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Blockchain-Empowered Decentralized Horizontal Federated Learning for 5G-Enabled UAVs

Cited by 132 publications

References 22 publications

Secure UAV-Aided Mobile Edge Computing for IoT: A Review

Secure UAV-Aided Mobile Edge Computing for IoT: A Review

Edge YOLO: Real-Time Intelligent Object Detection System Based on Edge-Cloud Cooperation in Autonomous Vehicles

A Data Management Model for Intelligent Water Project Construction Based on Blockchain

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