Towards Fully Explainable 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 11 in Security and Resilience in Distributed Machine Learning, 2026, pp 217-223 from Springer

Abstract: Abstract This chapter outlines a practical path toward fully explainable FL: we connect client-level attributions and visual explanations (e.g., LayerCAM-based heat maps) with representation-level diagnostics and lightweight, server-side screening that informs aggregation, fairness, and privacy decisions. Emphasizing deployment-ready tools with minimal overhead, we show how explainability improves robustness to poisoning, accelerates convergence under Non-IID data, and supports energy- and carbon-aware orchestration, thereby turning transparency from a reporting afterthought into a core mechanism for trustworthy FL.

Date: 2026
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DOI: 10.1007/978-3-032-23959-4_11

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