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PoShapley-BCFL: a Fair and Robust Decentralized Federated Learning Based on Blockchain and the Proof of Shapley-Value

EasyChair Preprint no. 11114

19 pagesDate: October 23, 2023


Recently, blockchain-based Federated learning(BCFL) has emerged as a promising technology for promoting data sharing in the Internet of Things(IoT) without relying a central authority, while ensuring data privacy, security, and traceability. However, it remains challenging to design an decentralized and appropriate incentive scheme that should promise a fair and efficient contribution evaluation for participants while defending against low-quality data attacks. Although Shapley-Value(SV) methods have been widely adopted in FL due to their ability to quantify individuals' contributions, they rely on a central server for calculation and incur high computational costs, making it impractical for decentralized and large-scale BCFL scenarios. In this paper, we designed and evaluated PoShapley-BCFL, a new blockchain-based FL approach to accommodate both contribution evaluation and defense against inferior data attacks. Specifically, we proposed PoShapley, a Shapley-value-enabled blockchain consensus protocol tailored to support a fair and efficient contribution assessment in PoShapley-BCFL. It mimics the Proof-of-Work mechanism that allows all participants to compute contributions in parallel based on an improved lightweight SV approach. Following using the PoShapley protocol, we further designed a fair-robust aggregation rule to improve the robustness of PoShapley-BCFL when facing inferior data attacks. Extensive experimental results validate the accuracy and efficiency of PoShapley in terms of distance and time cost, and also demonstrate the robustness of our designed PoShapley-BCFL.

Keyphrases: Blockchain, Contribution Evaluation, Federated Learning, Proof of Shapley

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Ziwen Cheng and Yi Liu and Chao Wu and Yongqi Pan and Liushun Zhao and Cheng Zhu},
  title = {PoShapley-BCFL: a Fair and Robust Decentralized Federated Learning Based on Blockchain and the Proof of Shapley-Value},
  howpublished = {EasyChair Preprint no. 11114},

  year = {EasyChair, 2023}}
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