Mechanism Design for An Incentive-aware Blockchain-enabled Federated Learning Platform

Toyoda, Kentaroh; Zhang, Allan N.

doi:10.1109/bigdata47090.2019.9006344

Cited by 81 publications

(73 citation statements)

References 12 publications

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“…The aim of this protocol is to mitigate the computational consumption in solving hashing-based puzzles and still ensure the data integrity. Toyoda et al [78] improved the common incentive mechanism in the current blockchain network and make it more applicable to the blockchain network when the machine learning tasks are involved.…”

Section: Blockchain For System Improvementmentioning

confidence: 99%

Blockchain-Empowered Mobile Edge Intelligence, Machine Learning and Secure Data Sharing

Du¹,

Miao²,

Tong³

et al. 2022

Blockchain Potential in AI

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Driven by recent advancements in machine learning, mobile edge computing (MEC) and the Internet of things (IoT), artificial intelligence (AI) has become an emerging technology. Traditional machine learning approaches require the training data to be collected and processed in centralized servers. With the advent of new decentralized machine learning approaches and mobile edge computing, the IoT on-device data training has now become possible. To realize AI at the edge of the network, IoT devices can offload training tasks to MEC servers. However, those distributed frameworks of edge intelligence also introduce some new challenges, such as user privacy and data security. To handle these problems, blockchain has been considered as a promising solution. As a distributed smart ledger, blockchain is renowned for high scalability, privacy-preserving, and decentralization. This technology is also featured with automated script execution and immutable data records in a trusted manner. In recent years, as quantum computers become more and more promising, blockchain is also facing potential threats from quantum algorithms. In this chapter, we provide an overview of the current state-of-the-art in these cutting-edge technologies by summarizing the available literature in the research field of blockchain-based MEC, machine learning, secure data sharing, and basic introduction of post-quantum blockchain. We also discuss the real-world use cases and outline the challenges of blockchain-empowered intelligence.

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Section: Blockchain For System Improvementmentioning

confidence: 99%

Blockchain-Empowered Mobile Edge Intelligence, Machine Learning and Secure Data Sharing

Du¹,

Miao²,

Tong³

et al. 2022

Blockchain Potential in AI

View full text Add to dashboard Cite

show abstract

“…where u(r j ) is a von Neumann-Morgenstern utility function, which is used to reflect the worker's risk for a reward [26]. 4 Since rewards are not necessarily guaranteed for a contribution, we assume that workers are risk-averse. This means that the expected payoff is not linear, and u(•) is monotonously increasing and concave.…”

Section: B Individual Rationalitymentioning

confidence: 99%

“…However, no existing work has theoretically clarified how rewarding impacts workers' behavior and what the optimal conditions are. Previously, we introduced a concept of competition to the model update process that allows only workers who have contributed to earn rewards [4]. However, the previous work was somewhat incomplete, and the following problems remain unsolved and questions unanswered:…”

Section: Introductionmentioning

confidence: 99%

“…First, when K ′ = 5, from Preposition 2, the optimal number of prizes is determined toj = 2, and the optimal reward proportion is given as r 1 = 2 3 , r 2 = 1 3 , and r j = 0 for 3 ≤ j ≤ 5. Similarly, when 4 10 , 3 10 , 2 10 , 1 10 }, and r j = 0 for 5 ≤ j ≤ 9. These results do not violate the reward policy, that is, r 1 ≤ r 2 ≤ • • • ≤ r K ′ ≤ 0, and might be quite intuitive.…”

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confidence: 99%

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Blockchain-Enabled Federated Learning With Mechanism Design

et al. 2020

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“…To analyze the collected big data and obtain useful information for detection, classification, and prediction, traditional machine learning techniques need to aggregate massive user data with personal information into a central server to perform model training. However, this classical centralized-learning paradigm causes excessive computation and storage cost due to the increasing data size, and the mobile devices also suffer from serious privacy leakage risk [3]- [4].…”

Section: Introductionmentioning

confidence: 99%

Incentive Design and Differential Privacy Based Federated Learning: A Mechanism Design Perspective

Kim

2020

IEEE Access

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Due to stricter data management regulations and large size of the training data, distributed learning paradigm such as federated learning (FL) has gained attention recently. FL is capable of significantly preserving end-users' private data from being exposed to external adversaries. However, private information can still be divulged by uploading parameters from users. Therefore, a key challenge in the FL platform is how users participate to build a high-quality learning model with effectively preventing information leakage. To address the above challenge, we design a novel incentive mechanism to attract more data owners to join in the FL process with the consideration of privacy preservation. To implement our proposed scheme, we adopt the concepts of mechanism design (MD) and differential privacy (DP); MD takes an objectives-first approach to designing incentives toward desired objectives, and the DP can provide a theoretical guarantee for users' privacy in FL participations. Based on the DP based incentive mechanism, our joint approach can leverage the full synergy that gives mutual advantages for users and learning operators. Therefore, we can take various benefits in a rational way under the dynamic changing FL environments. Through simulation analysis, the numerical results validate the performance efficiency of our proposed scheme.

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Mechanism Design for An Incentive-aware Blockchain-enabled Federated Learning Platform

Cited by 81 publications

References 12 publications

Blockchain-Empowered Mobile Edge Intelligence, Machine Learning and Secure Data Sharing

Blockchain-Empowered Mobile Edge Intelligence, Machine Learning and Secure Data Sharing

Blockchain-Enabled Federated Learning With Mechanism Design

Incentive Design and Differential Privacy Based Federated Learning: A Mechanism Design Perspective

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