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FedBC: An Efficient and Privacy-Preserving Federated Consensus Scheme

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Security and Privacy in Social Networks and Big Data (SocialSec 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1663))

Abstract

The capacity of federated learning (FL) to tackle the issue of “Data Island” while maintaining data privacy has garnered significant attention. Nonetheless, semi-trusted cloud platforms can infer the actual data distribution of local users via intermediate characteristics such as gradients. The blockchain proposal has resolved the challenge of consistency in decentralized data sharing. It is difficult to guarantee the accuracy of the block’s data based on the existing study. To address this issue, we present a federated consensus mechanism that is both efficient and protective of privacy (FedBC). This approach can effectively limit the impact of Byzantine nodes on consistency and accuracy. During this procedure, crucial intermediate parameters, such as the gradient of the data owner, will not leak. Specifically, we proposed a gradient-similarity-based secure consensus technique (SecPBFT) to minimize Byzantine gradients. All nodes transmit the DO sub-gradients during each consensus round and cluster and partition the sub-gradients in the consensus with care. Then, the dynamic elimination of Byzantine gradients in each round of the consensus procedure is accomplished. Theoretically, we demonstrated the scheme’s security and confirmed the scheme’s efficacy. FedBC’s attack success rate is at least 50% lower than if no defense mechanisms were in place.

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Acknowledgment

This work was supported by the National Natural Science Foundation of China (62125205), the Natural Science Basic Research Plan in Shaanxi Province (2022JQ-594).

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Authors and Affiliations

  1. School of Computer Science, Shaanxi Normal University, Xi’an, China

    Mengfan Xu

  2. School of Cyber Engineering, Xidian University, Xi’an, China

    Xinghua Li

Authors
  1. Mengfan Xu
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  2. Xinghua Li
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Correspondence to Mengfan Xu .

Editor information

Editors and Affiliations

  1. Xidian University, Xi’an, China

    Xiaofeng Chen

  2. Fujian Normal University, Fuzhou, China

    Xinyi Huang

  3. Wrocław University of Science and Technology, Wrocław, Poland

    Mirosław Kutyłowski

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© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Xu, M., Li, X. (2022). FedBC: An Efficient and Privacy-Preserving Federated Consensus Scheme. In: Chen, X., Huang, X., Kutyłowski, M. (eds) Security and Privacy in Social Networks and Big Data. SocialSec 2022. Communications in Computer and Information Science, vol 1663. Springer, Singapore. https://doi.org/10.1007/978-981-19-7242-3_10

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  • DOI: https://doi.org/10.1007/978-981-19-7242-3_10

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-7241-6

  • Online ISBN: 978-981-19-7242-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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