Resilience and Security of Graph-Based 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 3 in Security and Resilience in Distributed Machine Learning, 2026, pp 19-27 from Springer

Abstract: Abstract This chapter explores the role of graph-based methods in strengthening resilience and security within FL systems. Given the highly distributed and heterogeneous nature of FL, graph representations provide a powerful tool to model relationships among clients, data distributions, and model updates [10]. By capturing structural dependencies through graph neural networks (GNNs), VGAEs, and attention mechanisms, adversarial behaviors such as poisoning or inference attacks can be more effectively detected and mitigated. This chapter examines how graph-based modeling enhances the robustness of aggregation, supports anomaly detection, and facilitates secure knowledge transfer in dynamic and resource-constrained environments.

Date: 2026
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DOI: 10.1007/978-3-032-23959-4_3

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