Blockchain-Enabled Federated Learning With Mechanism Design

Toyoda, Kentaroh; Zhao, Jun; Zhang, Allan N.; Mathiopoulos, P. Takis

doi:10.1109/access.2020.3043037

Cited by 49 publications

(46 citation statements)

References 29 publications

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“…In [ 36 ], authors have provided a FL protocol to Encourage workers which collect and store models in blockchain-enabled FL. In this paper, they propose a mechanism-design-oriented FL protocol (MD) on a Blockchain model to make a rule.…”

Section: Research Methodology and Finding Appropriate Contentmentioning

confidence: 99%

Blockchain-based federated learning methodologies in smart environments

2021

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Section: Research Methodology and Finding Appropriate Contentmentioning

confidence: 99%

Blockchain-based federated learning methodologies in smart environments

2021

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“…The current, reliable methods mainly include a joint learning framework based on Blockchain protocol (Ma et al 2021) and a new measurement standard based on verification error (Martinez et al 2019). Similarly, some of these methods introduce the concept of competition to prevent workers from deviating from the protocol (Ogiela et al 2016), rewarding only those who contribute (Toyoda et al 2020).…”

Section: Assessing Client Contributionmentioning

confidence: 99%

Blockchain for federated learning toward secure distributed machine learning systems: a systemic survey

et al. 2021

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“…Consortium blockchain was further introduced by them for reputation management. Moreover, blockchain-enabled reward systems were considered in Reference [4,[46][47][48][49][50]. Furthermore, the blockchain-enabled data integrity and sources validation could be realized by deep learning with convolutional neural networks [51], giving the trustworthiness of training data quality.…”

Section: Blockchain-enabled Aimentioning

confidence: 99%

Blockchain-Enabled Edge Intelligence for IoT: Background, Emerging Trends and Open Issues

Wang

Leung

2021

Future Internet

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Blockchain, a distributed ledger technology (DLT), refers to a list of records with consecutive time stamps. This decentralization technology has become a powerful model to establish trust among trustless entities, in a verifiable manner. Motivated by the recent advancement of multi-access edge computing (MEC) and artificial intelligence (AI), blockchain-enabled edge intelligence has become an emerging technology for the Internet of Things (IoT). We review how blockchain-enabled edge intelligence works in the IoT domain, identify the emerging trends, and suggest open issues for further research. To be specific: (1) we first offer some basic knowledge of DLT, MEC, and AI; (2) a comprehensive review of current peer-reviewed literature is given to identify emerging trends in this research area; and (3) we discuss some open issues and research gaps for future investigations. We expect that blockchain-enabled edge intelligence will become an important enabler of future IoT, providing trust and intelligence to satisfy the sophisticated needs of industries and society.

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Blockchain-Enabled Federated Learning With Mechanism Design

Cited by 49 publications

References 29 publications

Blockchain-based federated learning methodologies in smart environments

Blockchain-based federated learning methodologies in smart environments

Blockchain for federated learning toward secure distributed machine learning systems: a systemic survey

Blockchain-Enabled Edge Intelligence for IoT: Background, Emerging Trends and Open Issues

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