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Data-Agnostic MP Techniques

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

The use of mobile edge computing is increasingly prevalent, especially in catering to user devices that come with a multitude of sensors. These sensors produce vast amounts of data, like images recording human activities or the real-time locations of vehicles, as seen in smart city scenarios [22, 55]. However, transferring this training data from the user’s device to a server can pose a threat to data privacy. FL is an emerging distributed ML approach that gains traction as a solution to mitigate data privacy concerns [20]. With FL, user devices can jointly train an ML model without having to disclose their private data to a server. The user devices, acting as clients, iteratively train their local models on their private data and send the local model updates to a server.

Suggested Citation

  • Kai Li & Xin Yuan & Wei Ni, 2026. "Data-Agnostic MP Techniques," Springer Series in Reliability Engineering, in: Security and Resilience in Distributed Machine Learning, chapter 6, pages 81-111, Springer.
    • Handle: RePEc:spr:ssrchp:978-3-032-23959-4_6
    DOI: 10.1007/978-3-032-23959-4_6
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