MsaaDI: A Heterogeneity-Resilient Federated Learning Framework for IoT Device Identification With Multi-Scale Adaptive Aggregation

Abstract

Accurate IoT device identification is critical to network forensics, access control, and anomaly detection in large-scale, security-sensitive environments. Federated learning (FL) provides a decentralized and privacy-enhancing approach well suited to IoT, but FL-based identification remains hampered by cross-client data heterogeneity, i.e., Non-IID distributions, and intra-client class imbalance, which jointly degrade global model performance and stability. To address these challenges, we propose MsaaDI, a novel federated device identification framework featuring Multi-Scale Adaptive Aggregation (MSAA) on the server side and a lightweight device fingerprinting pipeline with an enhanced local training strategy on the client side. The MSAA mechanism employs a three-stage aggregation scheme with real-time monitoring that robustly reweights heterogeneous client updates and refines global parameters to mitigate cross-client distribution skew, while the client design produces compact grayscale-image representations and alleviates intra-client imbalance and improves cross-client model consistency during local optimization. Extensive experiments on the UNSW and Aalto IoT device datasets show that, under extreme label-skewed Non-IID conditions with only 20% training data, MsaaDI achieves accuracies of 94.35% and 87.72%, respectively. It delivers up to 14.33% absolute improvement over the best-performing baseline, DA-PFL, and exhibits faster convergence and higher robustness. These results demonstrate MsaaDI’s effectiveness and adaptability for reliable IoT device identification in realistic deployments.