Federated Distributed Network Traffic Classification Based on Deep Mutual Learning

Abstract

As encrypted traffic analysis becomes increasingly vital for network security, the conventional reliance on centralized classification faces growing challenges due to data privacy regulations and data silos across heterogeneous nodes. Federated learning (FL) emerges as a solution by training models locally and sharing only parameter updates, thus preserving privacy. However, its performance is significantly degraded by data heterogeneity (i.e., non-IID data) among participants. To address this critical challenge, this paper proposes a Federated Learning framework based on Deep Mutual Learning (FLDML). In this method, clients first train local models on their private traffic data and then upload them to a server. There, they engage in deep mutual learning through co-training on a shared public dataset to enhance robustness and mitigate data heterogeneity. Subsequently, a global classifier is generated by averaging the model parameters. When evaluated on the ISCX VPN-NonVPN 2016 dataset, FLDML demonstrates significantly superior performance in handling non-IID traffic data compared to classical FL algorithms. This study concludes that the proposed framework not only effectively mitigates data heterogeneity in federated scenarios but also provides a scalable and improved solution for distributed network traffic classification.