HBMD-FL: Heterogeneous federated learning algorithm based on blockchain and model distillation

Ye LI, Jiale ZHANG, Junwu ZHU, Wenjuan LI

Research output: Chapter in Book/Report/Conference proceedingChapters

1 Citation (Scopus)

Abstract

Federated learning is a distributed machine learning framework that allows participants to keep their privacy data locally. Traditional federated learning coordinates participants collaboratively train a powerful global model. However, this process has several problems: it cannot meet the heterogeneous model’s requirements, and it cannot resist poisoning attacks and single-point-of-failure. In order to resolve these issues, we proposed a heterogeneous federated learning algorithm based on blockchain and model distillation. The problem of fully heterogeneous models that are hard to aggregate in the central server can be solved by leveraging model distillation technology. Moreover, blockchain replaces the central server in federated learning to solve the single-point-of-failure problem. The validation algorithm is combined with cross-validation, which helps federated learning to resist poison attacks. The extensive experimental results demonstrate that HBMD-FL can resist poisoning attacks while losing less than 3 $$\%$$ of model accuracy, and the communication consumption significantly outperformed the comparison algorithm. Copyright © 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG.

Original languageEnglish
Title of host publicationEmerging information security and applications: Third International Conference, EISA 2022, Wuhan, China, October 29–30, 2022, proceedings
EditorsJiageng CHEN, Debiao HE, Rongxing LU
Place of PublicationCham
PublisherSpringer
Pages145-159
ISBN (Electronic)9783031230981
ISBN (Print)9783031230974
DOIs
Publication statusPublished - 2022

Citation

Li, Y., Zhang, J., Zhu, J., & Li, W. (2022). HBMD-FL: Heterogeneous federated learning algorithm based on blockchain and model distillation. In J. Chen, D. He, & R. Lu (Eds.), Emerging information security and applications: Third International Conference, EISA 2022, Wuhan, China, October 29–30, 2022, proceedings (pp. 145-159). Springer. https://doi.org/10.1007/978-3-031-23098-1_9

Keywords

  • Federated learning
  • Blockchain
  • Heterogeneous
  • Model distillation

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