A Hierarchical Blockchain-Enabled Federated Learning Algorithm for Knowledge Sharing in Internet of Vehicles

Chai, Haoye; Leng, Supeng; Chen, Yijin; Zhang, Ke

doi:10.1109/tits.2020.3002712

Cited by 224 publications

(137 citation statements)

References 28 publications

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“…Experiments from practical datasets show the promising results of the proposed decentralized FL scheme with better accuracy and privacy protection over traditional centralized FL approaches. An FL scheme is also considered in [54] for knowledge sharing in vehicular networks with hierarchical blockchain. The proposed sharing architecture includes two main chain, i.e., ground chains and a top chain.…”

Section: A Fl Serving As An Alternative To Iot Data Sharingmentioning

confidence: 99%

“…Particularly, FL can be combined with other privacy techniques such as differential privacy [53] to improve the privacy of local updates, by integrating it into gradient descent training for enabling secure and robust FL sharing. Moreover, the security of FL-based data sharing can be improved by combining with the blockchain technology, as shown in [54]. In this context, the information of trained parameters can be appended into immutable blocks on the blockchain during the client-server communications.…”

Section: A Lessons Learned From Fl-iot Servicesmentioning

confidence: 99%

See 1 more Smart Citation

Federated Learning for Internet of Things: A Comprehensive Survey

Nguyen

Ding

Pathirana

et al. 2021

IEEE Commun. Surv. Tutorials

684

182

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Section: A Fl Serving As An Alternative To Iot Data Sharingmentioning

confidence: 99%

Section: A Lessons Learned From Fl-iot Servicesmentioning

confidence: 99%

Federated Learning for Internet of Things: A Comprehensive Survey

Nguyen

Ding

Pathirana

et al. 2021

IEEE Commun. Surv. Tutorials

684

182

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“…In [39], physical-layer assisted privacy-preserving offloading schemes was proposed and two efficient algorithms are developed to address the corresponding optimization problems by exploiting the favorable structure of the privacy-preserving offloading problem in the delay optimal and the energy optimal scenarios. In [40], a hierarchical blockchain-enabled federated learning algorithm for knowledge sharing is proposed in IoVs. The hierarchical blockchain framework is able to not only improve the reliability and security of knowledge sharing, but also adapt to the large scale vehicular networks with various regional characteristics.…”

Section: Related Workmentioning

confidence: 99%

“…cell , and the diameter coverage is 100m, 120m, 150m, 230m, 200m, 250m, 310m, respectively. The number of arriving vehicles is [1,40], and the speed range is [12,34]m/s. Then, we perform the task pre-allocation algorithm, comprehensively considering the vehicle speed, the range of each cell, the communication capability of the network access points and the computing power of the edge servers in the cell, etc., to predict the amount of tasks that the vehicle can execute in each cell.…”

Section: Simulation Scenariosmentioning

confidence: 99%

Joint optimization of network selection and task offloading for vehicular edge computing

Tang

Zhang

et al. 2021

J Cloud Comp

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Taking the mobile edge computing paradigm as an effective supplement to the vehicular networks can enable vehicles to obtain network resources and computing capability nearby, and meet the current large-scale increase in vehicular service requirements. However, the congestion of wireless networks and insufficient computing resources of edge servers caused by the strong mobility of vehicles and the offloading of a large number of tasks make it difficult to provide users with good quality of service. In existing work, the influence of network access point selection on task execution latency was often not considered. In this paper, a pre-allocation algorithm for vehicle tasks is proposed to solve the problem of service interruption caused by vehicle movement and the limited edge coverage. Then, a system model is utilized to comprehensively consider the vehicle movement characteristics, access point resource utilization, and edge server workloads, so as to characterize the overall latency of vehicle task offloading execution. Furthermore, an adaptive task offloading strategy for automatic and efficient network selection, task offloading decisions in vehicular edge computing is implemented. Experimental results show that the proposed method significantly improves the overall task execution performance and reduces the time overhead of task offloading.

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“…It integrated the blockchain mining, reinforcement learning, and the genetic algorithm to maximize the long-term offloading performance. Chai et al [7] introduced a hierarchical federated learning for vehicle knowledge sharing platforms. In the same context, Lu et al [32] suggested the blockchain empowered asynchronous federated learning-based solution for secure data sharing in Internet of vehicles.…”

Section: Blockchain Learningmentioning

confidence: 99%

SS-ITS: secure scalable intelligent transportation systems

et al. 2021

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This paper introduces a secure and scalable intelligent transportation and human behavior system to accurately discover knowledge from urban traffic data. The data are secured using blockchain learning technology, where the scalability is ensured by a threaded GPU. In addition, different optimizations are provided to efficiently process data on the GPU. A reinforcement deep learning algorithm is also established to merge local knowledge discovered on each site into global knowledge. To demonstrate the applicability of the proposed framework, intensive experiments have been carried out on well-known intelligent transportation and human behavior data. Our results show that our proposed framework outperforms the baseline solutions for the outlier detection use case.

show abstract

A Hierarchical Blockchain-Enabled Federated Learning Algorithm for Knowledge Sharing in Internet of Vehicles

Cited by 224 publications

References 28 publications

Federated Learning for Internet of Things: A Comprehensive Survey

Federated Learning for Internet of Things: A Comprehensive Survey

Joint optimization of network selection and task offloading for vehicular edge computing

SS-ITS: secure scalable intelligent transportation systems

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