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Exploring Visual Explanations for Attack Detection

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

FL has surfaced as a notably distributed ML framework, where user devices collaboratively engage in the training of a shared ML model, supervised by a server. User devices in FL consecutively train local model updates (e.g., weight parameters or gradients) utilizing their proprietary data. Rather than transmitting raw, private data, user devices forward model updates to a server for amalgamation. In response, the server integrates local model updates to generate a comprehensive global model that is then dispatched to the devices for updating their respective local models [12, 23]. Such a communication cycle repeats until the model achieves a satisfactory accuracy level. FL prevents the potential unauthorized dissemination of private data [46]. For instance, FL allows multiple medical institutions to collaboratively train a unified ML model without directly sharing sensitive patient data.

Suggested Citation

  • Kai Li & Xin Yuan & Wei Ni, 2026. "Exploring Visual Explanations for Attack Detection," Springer Series in Reliability Engineering, in: Security and Resilience in Distributed Machine Learning, chapter 8, pages 149-176, Springer.
    • Handle: RePEc:spr:ssrchp:978-3-032-23959-4_8
    DOI: 10.1007/978-3-032-23959-4_8
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