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Author:

Yang, Ruizhe (Yang, Ruizhe.) | Zhao, Tonghui (Zhao, Tonghui.) | Yu, F. Richard (Yu, F. Richard.) | Li, Meng (Li, Meng.) | Zhang, Dajun (Zhang, Dajun.) | Zhao, Xuehui (Zhao, Xuehui.)

Indexed by:

EI Scopus SCIE

Abstract:

Federated learning, leveraging distributed data from multiple nodes to train a common model, allows for the use of more data to improve the model while also protecting the privacy of original data. However, challenges still exist in ensuring privacy and security within the interactions. To address these issues, this article proposes a federated learning approach that incorporates blockchain, homomorphic encryption, and reputation. Using homomorphic encryption, edge nodes possessing local data can complete the training of ciphertext models, with their contributions to the aggregation being evaluated by a reputation mechanism. Both models and reputations are documented and verified on the blockchain through the consensus process, which then determines the rewards based on the incentive mechanism. This approach not only incentivizes participation in training, but also ensures the privacy of data and models through encryption. Additionally, it addresses security risks associated with both data and network attacks, ultimately leading to a highly accurate trained model. To enhance the efficiency of learning and the performance of the model, a joint adaptive aggregation and resource optimization algorithm is introduced. Finally, simulations and analyses demonstrate that the proposed scheme enhances learning accuracy while maintaining privacy and security.

Keyword:

Industrial Internet of Things (IIoT) federated learning security Blockchain privacy

Author Community:

  • [ 1 ] [Yang, Ruizhe]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 2 ] [Zhao, Tonghui]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 3 ] [Li, Meng]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 4 ] [Zhao, Xuehui]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 5 ] [Yu, F. Richard]Shenzhen Univ, Shenzhen Key Lab Digital & Intelligent Technol & S, Shenzhen 518060, Peoples R China
  • [ 6 ] [Zhang, Dajun]Carleton Univ, Sch Informat Technol, Ottawa, ON K1S 5B6, Canada

Reprint Author's Address:

  • [Li, Meng]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China;;

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Source :

IEEE INTERNET OF THINGS JOURNAL

ISSN: 2327-4662

Year: 2024

Issue: 12

Volume: 11

Page: 21674-21688

1 0 . 6 0 0

JCR@2022

Cited Count:

WoS CC Cited Count: 21

SCOPUS Cited Count: 24

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 3

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