IDEAS home Printed from https://ideas.repec.org/h/spr/ssrchp/978-3-032-23959-4_11.html

Towards Fully Explainable Federated Learning

In: Security and Resilience in Distributed Machine Learning

Author

Listed:
  • Kai Li

    (University of Luxembourg, Interdisciplinary Centre for Security, Reliability and Trust (SnT))

  • Xin Yuan

    (Commonwealth Scientific and Industrial Research Organisation (CSIRO), Data61 Business Unit)

  • Wei Ni

    (Commonwealth Scientific and Industrial Research Organisation (CSIRO), Data61 Business Unit)

Abstract

This chapter outlines a practical path toward fully explainable FL: we connect client-level attributions and visual explanations (e.g., LayerCAM-based heat maps) with representation-level diagnostics and lightweight, server-side screening that informs aggregation, fairness, and privacy decisions. Emphasizing deployment-ready tools with minimal overhead, we show how explainability improves robustness to poisoning, accelerates convergence under Non-IID data, and supports energy- and carbon-aware orchestration, thereby turning transparency from a reporting afterthought into a core mechanism for trustworthy FL.

Suggested Citation

  • Kai Li & Xin Yuan & Wei Ni, 2026. "Towards Fully Explainable Federated Learning," Springer Series in Reliability Engineering, in: Security and Resilience in Distributed Machine Learning, chapter 11, pages 217-223, Springer.
    • Handle: RePEc:spr:ssrchp:978-3-032-23959-4_11
    DOI: 10.1007/978-3-032-23959-4_11
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    More about this item

    Statistics

    Access and download statistics

    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:spr:ssrchp:978-3-032-23959-4_11. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.