A Distribution-Aware Robust Federated Learning Framework for Mobile Edge Networks

Abstract (excerpt). Federated learning (FL) is a promising technology for achieving edge intelligence in mobile edge networks while preserving the privacy of local clients. However, a significant challenge in FL is the non-IID data across clients, which can lead to inconsistent updates between local and global models, ultimately hindering convergence. Furthermore, recent research has shown that FL models are susceptible to adversarial attacks, especially in non-IID scenarios, which can significantly impair their performance. This vulnerability poses further challenges to achieving robust and generalizable edge intelligence. In this paper, we first identify that the model’s predictions for classes with fewer samples are less confident compared to those with more samples, even in federated adversarial environments. Second, recognizing that adversarial training (AT) is an effective defense mechanism, we propose a distribution-aware …

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Recommended citation: Yu Qiao, Phuong-Nam Tran, Choong Seon Hong. (2025). "A Distribution-Aware Robust Federated Learning Framework for Mobile Edge Networks." 2025 International Conference on Information Networking (ICOIN), 256-261.
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