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Resilience and Security of Graph-Based Federated Learning

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

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.

Suggested Citation

  • Kai Li & Xin Yuan & Wei Ni, 2026. "Resilience and Security of Graph-Based Federated Learning," Springer Series in Reliability Engineering, in: Security and Resilience in Distributed Machine Learning, chapter 3, pages 19-27, Springer.
    • Handle: RePEc:spr:ssrchp:978-3-032-23959-4_3
    DOI: 10.1007/978-3-032-23959-4_3
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