Dual-Factor Adaptive Robust Aggregation for Secure Federated Learning in IoT Networks

Federated Learning (FL) has been widely adopted in privacy-sensitive and distributed environments. However, training stability becomes significantly challenged when differential privacy (DP) noise and Byzantine client behaviors coexist, as these heterogeneous perturbations jointly introduce time-varying distortions to model updates. Existing approaches typically address privacy and robustness in isolation. Under DP constraints, noise injection increases gradient variance and obscures the distinction between benign and adversarial updates, causing many robust aggregation methods to misclassify normal clients or fail to detect malicious ones. As a result, their effectiveness degrades substantially in practical IoT environments where noise and attacks interact. In this work, we propose a dual-factor adaptive and robust aggregation framework (DARA) to improve the stability of FL under such combined disturbances. DARA adjusts the differential privacy noise scale by jointly considering local update magnitudes and training-round dynamics, aiming to mitigate noise-induced bias under a fixed privacy budget. Meanwhile, a direction-aware weighted aggregation scheme assigns continuous trust weights based on cosine similarity between updates, thereby suppressing the influence of potentially anomalous or adversarial clients. We conduct extensive experiments on multiple benchmark datasets to evaluate DARA under differential privacy constraints and Byzantine attack scenarios. The results indicate that DARA achieves favorable robustness and convergence behavior compared with representative aggregation baselines, while maintaining competitive model accuracy.