Cross-Silo Model-Based Secure Federated Transfer Learning for Flow-Based Traffic Classification

Traffic classification is crucial for autonomous network management. Deep learning-based traffic classification methods are in demand because of their ability to accurately classify even encrypted traffic. Federated learning is a way to collaboratively train learning models with privacy-preservation. Transfer learning allows learning models to share knowledge between tasks from different but related domains. Federated Transfer Learning allows collaborative training of privacy-preserving models with knowledge sharing from source to target domains. In this paper, we did secure federated transfer learning for improvising the training-time and accuracy of the target-federated-model for traffic classification. The target-federatedmodel outperforms the baseline-federated-model trained from scratch. We implemented a simple cross-silo secure aggregation protocol for security.

Automated summary

This paper discusses a secure traffic classification model optimization method based on federated transfer learning, which improves the performance of the target federated model through collaborative training. In addition, the use of deep learning and transfer learning techniques in traffic classification improves classification accuracy.