A Blockchain-Empowered Cluster-Based Federated Learning Model for Blade Icing Estimation on IoT-Enabled Wind Turbine

Cheng, Xu; Tian, Weiwei; Shi, Fan; Zhao, Meng; Chen, Shengyong; Wang, Hao

doi:10.1109/tii.2022.3159684

Cited by 18 publications

(1 citation statement)

References 39 publications

(50 reference statements)

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“…Bauca et al 47 developed a platform using federated learning and private blockchain technology in IoT to address privacy concerns and protect data security within the network. Cheng et al 48 introduced the BLADE model, a blockchain-authorized imbalanced federated learning model, to detect blade icing in wind turbines. This model integrates a verification mechanism to defend against poisoning attacks and employs an imbalanced learning algorithm to handle class imbalances in sensor data.…”

Section: Applications Of Btfl In the Field Of Internet Of Things (Iot)mentioning

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 the Field Of Internet Of Things (Iot)mentioning

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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FedSteg: Coverless Steganography‐Based Privacy‐Preserving Decentralized Federated Learning

Xu,

Lin

2024

IEEJ Transactions Elec Engng

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Federated learning (FL) represents a novel privacy‐preserving learning paradigm that offers a practical solution for distributed privacy preservation. Although privacy‐preserving FL based on homomorphic encryption (HE‐PPFL) exhibits resistance to gradient leakage attacks while ensuring the accuracy of aggregation results, its widespread adoption in blockchain privacy preservation is hindered by the reliance on a trusted key generation center and secure transfer channels. Conversely, coverless steganography schemes effectively ensure the covert transmission of sensitive information across insecure channels. However, their incompatibility with HE‐PPFL arises from the lossy extraction process. To address these challenges, we present a decentralized federated learning privacy‐preserving framework based on the Lifted ElGamal threshold decryption cryptosystem. We introduce a reversible steganography method tailored to safeguard gradient privacy. Furthermore, we introduce a lightweight, secure blind aggregation algorithm founded on the Raft protocol, which serves to protect gradient privacy while substantially mitigating computational overhead. Finally, we provide rigorous theoretical proof of the security and correctness of our proposed scheme. Experimental results from four public data sets demonstrate that our proposed scheme achieves a 100% extraction accuracy without the need for lossless methods, while simultaneously reducing the computational cost of ciphertext gradient aggregation by at least three orders of magnitude. The FedSteg framework is publicly accessible at https://github.com/Xumeili/FedSteg. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

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Enhancing trust and privacy in distributed networks: a comprehensive survey on blockchain-based federated learning

Liu,

Chen,

et al. 2024

Knowl Inf Syst

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A Blockchain-Empowered Cluster-Based Federated Learning Model for Blade Icing Estimation on IoT-Enabled Wind Turbine

Cited by 18 publications

References 39 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

FedSteg: Coverless Steganography‐Based Privacy‐Preserving Decentralized Federated Learning

Enhancing trust and privacy in distributed networks: a comprehensive survey on blockchain-based federated learning

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