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 privacypreserving 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 targetfederated-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.