An Optimized Federated Resource-Efficient IoT Security Framework with Uncertainty-Aware Distillation Loss - Okey, Ogobuchi Daniel; Rodríguez, Demóstenes Zegarra; Kleinschmidt, João Henrique (Anais do Simpósio Brasileiro de Cibersegurança (SBSeg); 2026: Anais do XXVI Simpósio Brasileiro de Cibersegurança, 2026)
This paper presents FedREUAD, a federated intrusion detection framework that jointly addresses client drift, communication overhead, and asymmetric knowledge transfer in distillation-based aggregation. Local model divergence under non-IID data is resolved through the FedProx’s proximal term that constrains local updates, limiting accuracy degradation on Dirichlet-simulated heterogeneity scenarios on CICIoT2023 and CICIoMT2024 datasets. During knowledge distillation, FedREUAD resolves the inherent asymmetry of Kullback-Leibler divergence (KL), which asymmetrically weights teacher-student discrepancies and propagates overconfident soft labels, by adopting a symmetric Jensen-Shannon divergence loss weighted by per-sample predictive uncertainty, penalizing the transfer of poorly calibrated soft labels from local teachers, thus reducing expected calibration error by 31% and 28% on the two datasets, respectively. Resource efficiency is achieved through distillation, structured magnitude pruning, and INT8 quantization, compressing local models by 90% and reducing per-round communication from 14.2 MB to 4.5 MB. The proposed method attains macro F1-scores of 98.22% on CICIoT2023 and 99.05% on CICIoMT2024, outperforming federated baselines under identical heterogeneity conditions, demonstrating that symmetric, uncertainty-calibrated distillation enables robust knowledge transfer without the calibration degradation inherent to asymmetric divergence formulations.