IoT: A Decentralized Trust Management System Using Blockchain-Empowered Federated Learning

Bi, Lirui; Muazu, Tasiu; Samuel, Omaji

doi:10.3390/su15010374

Cited by 9 publications

(3 citation statements)

References 38 publications

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“…Abou et al 54 designed a framework called HealthFed, utilizing federated learning and blockchain technology to achieve privacy protection and distributed learning among multiple clinical collaborators. Bi et al 55 introduced a decentralized federated learning medical trust management system based on blockchain, which uses a consortium blockchain formed by medical institutions to share data and collaboratively train without privacy leakage. Kumar 56 proposed a method that combines blockchain technology and trusted federated learning for collaborative training of a global model while protecting privacy, validated using real-world CT image data of COVID-19 patients.…”

Section: Applications Of Btfl In Medical Servicesmentioning

confidence: 99%

Towards trustworthy federated learning: a blockchain-based architecture for auditing, traceability, and verification

Gao,

Pan,

Zhang

et al. 2023

Fourth International Conference on Computer Science and Communication Technology (ICCSCT 2023)

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The 14th Five-Year Plan proposes the objective of expediting digital development and constructing a digital China, aiming to minimize the reliance on physical travel while fostering increased data flow and sharing. However, the implementation of data privacy regulations, such as the Personal Information Protection Law, has resulted in conflicts between data sharing and privacy. To address this challenge, federated learning technology has emerged as an effective solution that enables data sharing while safeguarding privacy. Nonetheless, trust issues arise among federated members in relation to each other and central servers. This is where blockchain technology, with its notable features of verifiability, auditability, and traceability, can play a pivotal role in resolving trust concerns within federated learning. Consequently, a novel framework called trusted federated learning based on blockchain has emerged. This article initially defines the trust issue in federated learning and subsequently provides a chronological overview of research advancements in the domain of trusted federated learning based on blockchain. Furthermore, it discusses the advantages and disadvantages of existing research across three categories: blockchain structure design, consensus mechanism, and smart contracts. Additionally, the article highlights the application characteristics of this framework in fields such as the Internet of Things, connected vehicles, and medical services. Finally, the core challenges and future research directions of trusted federated learning based on blockchain are outlined.

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Section: Applications Of Btfl In Medical Servicesmentioning

confidence: 99%

Towards trustworthy federated learning: a blockchain-based architecture for auditing, traceability, and verification

Gao,

Pan,

Zhang

et al. 2023

Fourth International Conference on Computer Science and Communication Technology (ICCSCT 2023)

View full text Add to dashboard Cite

show abstract

“…In [18], a decentralized-based trust management approach is proposed that utilizes blockchain and federated learning to maintain trustworthiness in an IoT environment. The proposed approach protocol consists of trust scores, trust deviation, and trust consistency values to perform computations in the blockchain.…”

Section: Background Studymentioning

confidence: 99%

“…We propose an ensemble learning approach for trust-based intrusion detection in IoT environment using knowledge, reputation, and experience as trust management components. Our approach is compared with two existing approaches, PoTC [18] and DDQN-Trust [20], for a comprehensive evaluation of its performance. The dataset consists of 19 trust features, and we employ an ANN as the base model.…”

Section: Experimental Simulation and Analysismentioning

confidence: 99%

Securing IoT With Deep Federated Learning: A Trust-Based Malicious Node Identification Approach

et al. 2023

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The Internet of Things (IoT) has revolutionized the world with its diverse applications and smart connected devices. These IoT devices communicate with each other without human intervention and make life easier in many ways. However, the independence of these devices raises several significant concerns, such as security and privacy preservation due to malicious and compromised nodes within the network. Trust management has been introduced as a less computationally intensive alternative to traditional approaches such as cryptography. The proposed FedTrust approach addresses these challenges by designing a method for identifying malicious and compromised nodes using federated learning. FedTrust trains edge nodes with a provided dataset and forms a global model to predict the abnormal behavior of IoT nodes. The proposed approach utilizes a novel trust dataset consisting of 19 trust parameters from three major components: knowledge, experience, and reputation. To reduce the computational burden, FedTrust employs the concept of communities with dedicated servers to divide the dataset into smaller parts for more efficient training. The proposed approach is extensively evaluated in comparison to existing approaches in terms of accuracy, precision, and other metrics to validate its performance in IoT networks. Simulation results demonstrate the effectiveness of FedTrust by achieving a higher rate of detection and prediction of malicious and compromised nodes.

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RETRACTED ARTICLE: Intelligent root cause detection in Industry 4.0: a secure cross-validation of blockchain architecture for product failure checker

Anu,

Renjith,

Agarkar

et al. 2023

Opt Quant Electron

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IoT: A Decentralized Trust Management System Using Blockchain-Empowered Federated Learning

Cited by 9 publications

References 38 publications

Towards trustworthy federated learning: a blockchain-based architecture for auditing, traceability, and verification

Towards trustworthy federated learning: a blockchain-based architecture for auditing, traceability, and verification

Securing IoT With Deep Federated Learning: A Trust-Based Malicious Node Identification Approach

RETRACTED ARTICLE: Intelligent root cause detection in Industry 4.0: a secure cross-validation of blockchain architecture for product failure checker

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