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Privacy-Enhanced Federated Learning for Non-IID Data

Author

Listed:
  • Qingjie Tan

    (School of Science, Zhejiang University of Science and Technology, Hangzhou 310023, China)

  • Shuhui Wu

    (School of Science, Zhejiang University of Science and Technology, Hangzhou 310023, China)

  • Yuanhong Tao

    (School of Science, Zhejiang University of Science and Technology, Hangzhou 310023, China)

Abstract

Federated learning (FL) allows the collaborative training of a collective model by a vast number of decentralized clients while ensuring that these clients’ data remain private and are not shared. In practical situations, the training data utilized in FL often exhibit non-IID characteristics, hence diminishing the efficacy of FL. Our study presents a novel privacy-preserving FL algorithm, HW-DPFL, which leverages data label distribution similarity as a basis for its design. Our proposed approach achieves this objective without incurring any additional overhead communication. In this study, we provide evidence to support the assertion that our approach improves the privacy guarantee and convergence of FL both theoretically and empirically.

Suggested Citation

  • Qingjie Tan & Shuhui Wu & Yuanhong Tao, 2023. "Privacy-Enhanced Federated Learning for Non-IID Data," Mathematics, MDPI, vol. 11(19), pages 1-21, September.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:19:p:4123-:d:1250906
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    References listed on IDEAS

    as
    1. Jianzhe Zhao & Keming Mao & Chenxi Huang & Yuyang Zeng & Shi Cheng, 2021. "Utility Optimization of Federated Learning with Differential Privacy," Discrete Dynamics in Nature and Society, Hindawi, vol. 2021, pages 1-14, October.
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