IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2024i16p2479-d1453914.html
   My bibliography  Save this article

EVFL: Towards Efficient Verifiable Federated Learning via Parameter Reuse and Adaptive Sparsification

Author

Listed:
  • Jianping Wu

    (College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China)

  • Chunming Wu

    (College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China)

  • Chaochao Chen

    (College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China)

  • Jiahe Jin

    (Key Laboratory of Key Technologies for Open Data Fusion in Zhejiang Province, Hangzhou 310007, China)

  • Chuan Zhou

    (School of Microelectronics, Tianjin University, Tianjin 300100, China
    School of Information Engineering, Minzu University of China, Beijing 100081, China)

Abstract

Federated learning (FL) demonstrates significant potential in Industrial Internet of Things (IIoT) settings, as it allows multiple institutions to jointly construct a shared learning model by exchanging model parameters or gradient updates without the need to transmit raw data. However, FL faces risks related to data poisoning and model poisoning. To address these issues, we propose an efficient verifiable federated learning (EVFL) method, which integrates adaptive gradient sparsification (AdaGS), Boneh–Lynn–Shacham (BLS) signatures, and fully homomorphic encryption (FHE). The combination of BLS signatures and the AdaGS algorithm is used to build a secure aggregation protocol. These protocols verify the integrity of parameters uploaded by industrial agents and the consistency of the server’s aggregation results. Simulation experiments demonstrate that the AdaGS algorithm significantly reduces verification overhead through parameter sparsification and reuse. Our proposed algorithm achieves better verification efficiency compared to existing solutions.

Suggested Citation

  • Jianping Wu & Chunming Wu & Chaochao Chen & Jiahe Jin & Chuan Zhou, 2024. "EVFL: Towards Efficient Verifiable Federated Learning via Parameter Reuse and Adaptive Sparsification," Mathematics, MDPI, vol. 12(16), pages 1-21, August.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:16:p:2479-:d:1453914
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/16/2479/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/12/16/2479/
    Download Restriction: no
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:12:y:2024:i:16:p:2479-:d:1453914. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.