Cross-Model Federated Learning-Based Network Traffic Classification

Network traffic classification (NTC) plays a pivotal role in areas such as service quality assurance, malicious activity detection, and lawful interceptions. However, the increasing complexity of network environments, amplified by diverse modalities of traffic data, poses significant challenges for conventional models. This study introduces a Federated Learning (FL) based framework to address cross-modal heterogeneity, where each client hosts data from distinct modalities. The approach overcomes key challenges by predicting a common feature space, learning modality-specific features, and aggregating diverse client parameters. Experimental results demonstrate the effectiveness of the proposed approach, achieving high accuracy, sensitivity, and F1-score across multiple configurations. Performance improves significantly by increasing training rounds, showcasing the framework’s capability to adapt and generalize across diverse data distributions. The study advances the understanding of real-world NTC scenarios, offering a scalable and privacy-preserving solution for cross-model heterogeneous FL environments.