AI at the Edge: Blockchain-Empowered Secure Multiparty Learning With Heterogeneous Models

Wang, Qianlong; Guo, Yifan; Wang, Xufei; Ji, Tianxi; Yu, Lixing; Li, Pan

doi:10.1109/jiot.2020.2987843

Cited by 62 publications

(21 citation statements)

References 18 publications

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“…The simulation results show that the proposed CrowdSFL protocol can resist the following malicious behaviors: Malicious miners, Malicious workers, and Malicious requesters. To resist poisoning attacks as well as membership inference attacks in 5G networks, Liu Wang et al [100] proposed a secure decentralized multiparty learning scheme, named BEMA, for edge computingbased IoT applications. Specifically, each part in the BEMA scheme distributes their local model and during that time, they are processing the models received from other users about their local dataset and identify the models that require certification.…”

Section: B Permissionless Blockchain-based Solutionsmentioning

confidence: 99%

“…This attacker can induce errors in their local model update process. Wang et al [100] designed a secure federated learning system based on blockchain technology that can defend against Byzantine attacks. Jebreel et al [128] designed a novel concept against Byzantine attacks where the basic concept is the analysis of a small fraction of the updates, instead of analyzing the whole updates.…”

Section: Byzantine Attackmentioning

confidence: 99%

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Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis

Ferrag¹,

et al. 2021

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In this article, we present a comprehensive study with an experimental analysis of federated deep learning approaches for cyber security in the Internet of Things (IoT) applications. Specifically, we first provide a review of the federated learning-based security and privacy systems for several types of IoT applications, including, Industrial IoT, Edge Computing, Internet of Drones, Internet of Healthcare Things, Internet of Vehicles, etc. Second, the use of federated learning with blockchain and malware/intrusion detection systems for IoT applications is discussed. Then, we review the vulnerabilities in federated learning-based security and privacy systems. Finally, we provide an experimental analysis of federated deep learning with three deep learning approaches, namely, Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), and Deep Neural Network (DNN). For each deep learning model, we study the performance of centralized and federated learning under three new real IoT traffic datasets, namely, the Bot-IoT dataset, the MQTTset dataset, and the TON_IoT dataset. The goal of this article is to provide important information on federated deep learning approaches with emerging technologies for cyber security. In addition, it demonstrates that federated deep learning approaches outperform the classic/centralized versions of machine learning (non-federated learning) in assuring the privacy of IoT device data and provide the higher accuracy in detecting attacks.

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Section: B Permissionless Blockchain-based Solutionsmentioning

confidence: 99%

Section: Byzantine Attackmentioning

confidence: 99%

Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis

Ferrag¹,

et al. 2021

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“…Plus, Yin et al [54] investigated a blockchain-based federated deep learning in the IoT domain. This strategy was motivated by multiparty secure computation, which was also investigated in Reference [55]. Besides, Liu et al [56] used smart contracts in the self-defense of FL.…”

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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“…In [63], the authors proposed a secure multiparty learning system called BEMA based on blockchain. is paper mentioned two forms of Byzantine attacks: (1) malicious participants broadcast a malicious local model to other parties for changing the outcomes of categorization;…”

Section: Decentralized Intelligencementioning

confidence: 99%

The Applications of Blockchain in Artificial Intelligence

Wang

Luo

Wen

et al. 2021

Security and Communication Networks

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There has been increased interest in applying artificial intelligence (AI) in various settings to inform decision-making and facilitate predictive analytics. In recent times, there have also been attempts to utilize blockchain (a peer-to-peer distributed system) to facilitate AI applications, for example, in secure data sharing (for model training), preserving data privacy, and supporting trusted AI decision and decentralized AI. Hence, in this paper, we perform a comprehensive review of how blockchain can benefit AI from these four aspects. Our analysis of 27 English-language articles published between 2018 and 2021 identifies a number of research challenges and opportunities.

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AI at the Edge: Blockchain-Empowered Secure Multiparty Learning With Heterogeneous Models

Cited by 62 publications

References 18 publications

Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis

Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis

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

The Applications of Blockchain in Artificial Intelligence

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