Literature Database Entry

li2025explainable

Kai Li, Jingjing Zheng, Wei Ni, Hailong Huang, Pietro Liò, Falko Dressler and Ozgur B. Akan, "Explainable Graph Attention-Driven Fairness Manipulation for Federated Learning in EdgeIoT," Proceedings of IEEE/CIC International Conference on Communications in China (ICCC 2025), Shanghai, China, August 2025. (to appear)

Abstract

This paper proposes an innovative adversarial architecture based on Explainable Graph AtTention-embedded autoEncoder (E-GATE), specifically designed to execute fairness manipulation that introduce biasing model updates into the federated learning in edge-based Internet of Things (EdgeIoT). E-GATE aims to generate biasing model updates by maximizing the minimum Kullback-Leibler (KL) divergence between a device's local model update and the global model. The E-GATE is trained with attention coefficients to obtain the hidden representations of each data feature in the explainable graph. Additionally, the graph autoencoder is incorporated within the E-GATE architecture to manipulatively reconstruct the correlations among model updates. This approach maximizes the reconstruction loss while keeping the biasing model updates undetected. The E-GATE attack is implemented using PyTorch, and experimental results demonstrate that it successfully increases the minimum KL divergence of benign model updates by 70.2%, effectively evading detection by existing defense mechanisms.

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Kai Li
Jingjing Zheng
Wei Ni
Hailong Huang
Pietro Liò
Falko Dressler
Ozgur B. Akan

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