Communication-Efficient Federated Learning and Permissioned Blockchain for Digital Twin Edge Networks

Lu, Yunlong; Huang, Xiaotao; Zhang, Ke; Maharjan, Sabita; Zhang, Yan

doi:10.1109/jiot.2020.3015772

Cited by 210 publications

(94 citation statements)

References 28 publications

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“…( 8) can be modified as where M is the total number of possible s m . Similar to Theorem 2, the existence of Nash Equilibrium point can be proved for U RLY defined in (16).…”

Section: B Economic Modelmentioning

confidence: 80%

See 1 more Smart Citation

A Blockchain Based Federated Learning for Message Dissemination in Vehicular Networks

Ayaz

Sheng

Tian

et al. 2022

IEEE Trans. Veh. Technol.

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“…( 8) can be modified as where M is the total number of possible s m . Similar to Theorem 2, the existence of Nash Equilibrium point can be proved for U RLY defined in (16).…”

Section: B Economic Modelmentioning

confidence: 80%

“…Therefore, they can be used to set rules for protecting FL from adversary and security attacks. The process of transaction verification in blockchain can also be utilized to validate local models in FL [16]. Table I summarizes the current issues of FL in IoV and corresponding solutions provided by blockchain.…”

Section: Introductionmentioning

confidence: 99%

A Blockchain Based Federated Learning for Message Dissemination in Vehicular Networks

Ayaz

Sheng

Tian

et al. 2022

IEEE Trans. Veh. Technol.

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“…In [51], authors proposed a blockchain-based FL for digital twin environment between edge computing and IoT systems, which integrates digital twins with edge networks. To improve communication security and data privacy protection in the proposed model, they propose a blockchain-enabled FL scheme.…”

Section: Blockchain-based Horizontal Federated Learning Techniquesmentioning

confidence: 99%

Blockchain-based federated learning methodologies in smart environments

2021

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“…Traditional learning methods are no longer suitable for largescale distributed AI collaboration among devices in open and dynamic edge environments. To facilitate the collaboration of the learning machines, a variety of algorithms and methods have been designed [171], [172]. For example, a configurable framework for training the AI model collaboratively on the BC is proposed to make model updates more efficient [121].…”

Section: Research Challenges and Future Directionsmentioning

confidence: 99%

Synergy of Edge Intelligence and Blockchain: A Comprehensive Survey

Wang¹,

Ren²,

Qiu³

et al. 2021

Preprint

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Edge intelligence (EI), as an emerging technology, has been attracting significant attention. It pushes the frontier of artificial intelligence (AI) from the cloud to the network edge, aiming to embrace and support the next-generation communications while unleashing AI services. However, it faces challenges in its decentralized management and security, which limit its capabilities to support services with numerous requirements. On the other side, blockchain (BC), as a promising decentralized technology, is beneficial to tackle the above issues. However, there exist some technical challenges for BC, such as transaction capacity, scalability, and fault tolerance. Motivated by significant current interest around EI and BC, this survey examines whether the synergy of EI and BC can make a powerful network with combined functionalities of both cutting-edge technologies. Accordingly, we develop EI-chain and Chain-intelligence to realize reliable computing-power management, data administration, and model optimization at the edges, while improving the functions of BC by leveraging complementary characteristics of EI and BC, further making up for their own limitations. In this survey, a wide spectrum of literature is carefully reviewed to enable the EI-chain and Chain-intelligence. Moreover, we cover the technological aspects of EI-chain and Chain-intelligence: overview, motivations, and frameworks. Finally, some challenges and future directions are explored. We believe this survey will provide developers and researchers a comprehensive view on the synergy of EI and BC, while accelerating the design of a powerful integrated network.

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Communication-Efficient Federated Learning and Permissioned Blockchain for Digital Twin Edge Networks

Cited by 210 publications

References 28 publications

A Blockchain Based Federated Learning for Message Dissemination in Vehicular Networks

A Blockchain Based Federated Learning for Message Dissemination in Vehicular Networks

Blockchain-based federated learning methodologies in smart environments

Synergy of Edge Intelligence and Blockchain: A Comprehensive Survey

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