Privacy-Aware Wireless Federated Learning

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 7 in Security and Resilience in Distributed Machine Learning, 2026, pp 115-148 from Springer

Abstract: Abstract As a framework for distributed online computing and model training, FL has shown significant potential for applications, e.g., IoT, autonomous driving, and remote medical care [24]. FL enables individual mobile clients to train a global model collectively without releasing their data [17]. In particular, each client trains its local model independently, relying on its local dataset, and sends the gradient of the local model to a server.

Date: 2026
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DOI: 10.1007/978-3-032-23959-4_7

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