Security and Resilience in Distributed Machine Learning

Kai Li (), Xin Yuan () and Wei Ni ()
Additional contact information
Kai Li: Carnegie Mellon University, College of Engineering
Xin Yuan: Commonwealth Scientific and Industrial Research Organisation (CSIRO), Data61 Business Unit
Wei Ni: Edith Cowan University, School of Engineering

in Springer Series in Reliability Engineering from Springer, currently edited by Hoang Pham

Date: 2026
ISBN: 978-3-032-23959-4
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Chapters in this book:

Ch 1 Introduction to Federated Learning

Kai Li, Xin Yuan and Wei Ni

Ch 2 Threat Landscape in Federated Learning

Kai Li, Xin Yuan and Wei Ni

Ch 3 Resilience and Security of Graph-Based Federated Learning

Kai Li, Xin Yuan and Wei Ni

Ch 4 Model Poisoning via Variational Graph Representations

Kai Li, Xin Yuan and Wei Ni

Ch 5 Biasing Federated Learning Based on Adversarial Graph Attention Networks

Kai Li, Xin Yuan and Wei Ni

Ch 6 Data-Agnostic MP Techniques

Kai Li, Xin Yuan and Wei Ni

Ch 7 Privacy-Aware Wireless Federated Learning

Kai Li, Xin Yuan and Wei Ni

Ch 8 Exploring Visual Explanations for Attack Detection

Kai Li, Xin Yuan and Wei Ni

Ch 9 Privacy-Utility Trade-Off in Federated Learning

Kai Li, Xin Yuan and Wei Ni

Ch 10 Integrating Graph Neural Networks with Federated Learning

Kai Li, Xin Yuan and Wei Ni

Ch 11 Towards Fully Explainable Federated Learning

Kai Li, Xin Yuan and Wei Ni

Ch 12 Sustainability in Federated Learning

Kai Li, Xin Yuan and Wei Ni

Ch 13 Summary and Outlook

Kai Li, Xin Yuan and Wei Ni

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DOI: 10.1007/978-3-032-23959-4

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