Adversarially Resilient Federated Learning for Heterogeneous Edge Nodes in 5G Networks with Non-IID Data

The rapid deployment of 5 G and Beyond-5 G ( B 5 G ) edge networks introduces unique challenges for federated learning (FL) frameworks deployed at the edge, primarily due to heterogeneous non-IID data distributions and adversarial vulnerabilities. This paper proposes an adversarially robust federated learning (ARFL) mechanism that integrates hybrid feature selection and adversarial optimization to jointly enhance robustness against adversarial perturbations and improve computational efficiency under heterogeneous data distributions. The proposed methodology jointly optimizes classifier and adversary in a min–max formulation to enable robustness against perturbations of varying strengths. Experimental results on a real-world intrusion detection 5G-NIDD dataset demonstrates that standard FL suffers drastic deterioration under adversarial conditions, with accuracy, precision, recall, and F1-scores dropping to 20%–30% at ϵ = 0.3 . In contrast, the proposed ARFL framework consistently sustains performance above 92% across these metrics under all non-IID distributions, highlighting its robustness and reliability. Overall, ARFL achieves absolute adversarial accuracy improvements of 20%–70% points over standard FL while incurring only a marginal reduction in clean performance. Scalability experiments demonstrate the stability and efficiency of the ARFL framework, underscoring its suitability for real-world 5 G edge deployments where robustness and efficiency are paramount.