Threat Landscape in 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 2 in Security and Resilience in Distributed Machine Learning, 2026, pp 9-18 from Springer

Abstract: Abstract Although artificial intelligence (AI)-enabled Internet-of-Things (IoT) systems increasingly employ ML to provide localized intelligence, the rising heterogeneity and dynamic characteristics of IoT data highlight the limitations of task-specific models. FL with the ability to generalize across domains and tasks through large-scale pretraining is being widely adopted to support IoT applications, such as predictive maintenance, healthcare monitoring, and autonomous mobility. Moreover, FL facilitates cross-domain knowledge transfer, thereby reducing the need to train models from scratch on every device. However, integrating FL into IoT introduces unique security concerns: unlike conventional AI systems, FL operates in distributed and resource-constrained environments, often relying on federated or decentralized training across IoT nodes. This distributed interaction exposes FL to IoT-specific threats, including model poisoning (MP), inference attacks, and adversarial manipulations, particularly under non-independent and identically distributed (non-IID) data distributions and intermittent connectivity. Consequently, addressing the security challenges of FL within IoT ecosystems is crucial for enabling trustworthy AI and ensuring the resilience of IoT infrastructures.

Date: 2026
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DOI: 10.1007/978-3-032-23959-4_2

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