A Hypernetwork-based Personalized Federated Learning Framework for Encrypted Traffic Classification

With the popularity of the Internet and the rapid devel- opment of software technology, the classification and analysis of en- crypted traffic has become crucial. Due to the complexity of network environments and private encryption protocols, traffic data in the open world usually exhibit high statistical heterogeneity. Thus, traditional traffic classification methods trained by single dataset will encounter difficulties of misclassification. This paper proposed a hypernetwork- based personalized federated learning framework for encrypted traffic classification (ETC). In this framework, hypernetworks on the server side can generate layer-granularity weights for model aggregation. Clients can not only focus on improving the effect on local datasets but also aggregate models from other clients more appropriately guided by hypernetworks. Experimental evaluations are conducted on real-world encrypted traffic datasets under different heterogeneous scenarios. The results show that our framework gains a 2-8% improvement over the state-of-the-art methods on ETC tasks.