Data-Agnostic MP Techniques

Kai Li (), Xin Yuan () and Wei Ni ()
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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

Chapter 6 in Security and Resilience in Distributed Machine Learning, 2026, pp 81-111 from Springer

Abstract: 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.

Date: 2026
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DOI: 10.1007/978-3-032-23959-4_6

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