HFL-FlowLLM: Large Language Models for Network Traffic Flow Classification in Heterogeneous Federated Learning

TL;DR

HFL-FlowLLM introduces a novel large language model-based federated learning framework for network traffic classification, significantly improving accuracy and reducing training costs in heterogeneous network environments.

Contribution

This paper pioneers the application of large language models to heterogeneous federated learning for network traffic classification, achieving notable performance gains.

Findings

Improves average F1 score by ~13% over state-of-the-art methods.

Achieves up to 5% higher F1 score with more clients.

Reduces training costs by approximately 87%.

Abstract

In modern communication networks driven by 5G and the Internet of Things (IoT), effective network traffic flow classification is crucial for Quality of Service (QoS) management and security. Traditional centralized machine learning struggles with the distributed data and privacy concerns in these heterogeneous environments, while existing federated learning approaches suffer from high costs and poor generalization. To address these challenges, we propose HFL-FlowLLM, which to our knowledge is the first framework to apply large language models to network traffic flow classification in heterogeneous federated learning. Compared to state-of-the-art heterogeneous federated learning methods for network traffic flow classification, the proposed approach improves the average F1 score by approximately 13%, demonstrating compelling performance and strong robustness. When compared to existing large language models federated learning frameworks, as the number of clients participating in each training round increases, the proposed method achieves up to a 5% improvement in average F1 score while reducing the training costs by about 87%. These findings prove the potential and practical value of HFL-FlowLLM in modern communication networks security.