IDEAS home Printed from https://ideas.repec.org/a/inm/orijds/v2y2023i1p10-23.html
   My bibliography  Save this article

GIFAIR-FL: A Framework for Group and Individual Fairness in Federated Learning

Author

Listed:
  • Xubo Yue

    (Industrial and Operations Engineering, University of Michigan, Ann Arbor, Michigan 48109)

  • Maher Nouiehed

    (Industrial Engineering and Management, American University of Beirut, Beirut 1107-2020, Lebanon)

  • Raed Al Kontar

    (Industrial and Operations Engineering, University of Michigan, Ann Arbor, Michigan 48109)

Abstract

In this paper, we propose GIFAIR - FL , a framework that imposes group and individual fairness (GIFAIR) to federated learning (FL) settings. By adding a regularization term, our algorithm penalizes the spread in the loss of client groups to drive the optimizer to fair solutions. Our framework GIFAIR-FL can accommodate both global and personalized settings. Theoretically, we show convergence in nonconvex and strongly convex settings. Our convergence guarantees hold for both independent and identically distributed (i.i.d.) and non-i.i.d. data. To demonstrate the empirical performance of our algorithm, we apply our method to image classification and text prediction tasks. Compared with existing algorithms, our method shows improved fairness results while retaining superior or similar prediction accuracy.

Suggested Citation

  • Xubo Yue & Maher Nouiehed & Raed Al Kontar, 2023. "GIFAIR-FL: A Framework for Group and Individual Fairness in Federated Learning," INFORMS Joural on Data Science, INFORMS, vol. 2(1), pages 10-23, April.
    • Handle: RePEc:inm:orijds:v:2:y:2023:i:1:p:10-23
    DOI: 10.1287/ijds.2022.0022
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/ijds.2022.0022
    Download Restriction: no
    File URL: https://libkey.io/10.1287/ijds.2022.0022?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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:inm:orijds:v:2:y:2023:i:1:p:10-23. 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: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    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.