# FedEdge AI-TC: A Semi-supervised Traffic Classification Method based on Trusted Federated Deep Learning for Mobile Edge Computing

arXiv: https://arxiv.org/abs/2308.06924 · HTML: https://mlsanity.com/paper/2308.06924/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 49 references that are in the map.
- First posted on arXiv 14 Aug 2023.

## Builds on (most cited first, 10 of 10)

- [Federated Learning for Wireless Communications: Motivation, Opportunities and Challenges](https://mlsanity.com/paper/1908.06847/) (2019)
- [Deep Learning for Encrypted Traffic Classification: An Overview](https://mlsanity.com/paper/1810.07906/) (2018)
- [How to Achieve High Classification Accuracy with Just a Few Labels: A Semi-supervised Approach Using Sampled Packets](https://mlsanity.com/paper/1812.09761/) (2018)
- [Deep Learning and Traffic Classification: Lessons learned from a commercial-grade dataset with hundreds of encrypted and zero-day applications](https://mlsanity.com/paper/2104.03182/) (2021)
- [PacketCGAN: Exploratory Study of Class Imbalance for Encrypted Traffic Classification Using CGAN](https://mlsanity.com/paper/1911.12046/) (2019)
- [A Federated Semi-Supervised Learning Approach for Network Traffic Classification](https://mlsanity.com/paper/2107.03933/) (2021)
- [CGNN: Traffic Classification with Graph Neural Network](https://mlsanity.com/paper/2110.09726/) (2021)
- [A Smart Home Gateway Platform for Data Collection and Awareness](https://mlsanity.com/paper/1804.01242/) (2018)
- [Deep Learning for Network Traffic Classification](https://mlsanity.com/paper/2106.12693/) (2021)
- [Federated Traffic Synthesizing and Classification Using Generative Adversarial Networks](https://mlsanity.com/paper/2104.10400/) (2021)

## Abstract (arXiv)

As a typical entity of MEC (Mobile Edge Computing), 5G CPE (Customer Premise Equipment)/HGU (Home Gateway Unit) has proven to be a promising alternative to traditional Smart Home Gateway. Network TC (Traffic Classification) is a vital service quality assurance and security management method for communication networks, which has become a crucial functional entity in 5G CPE/HGU. In recent years, many researchers have applied Machine Learning or Deep Learning (DL) to TC, namely AI-TC, to improve its performance. However, AI-TC faces challenges, including data dependency, resource-intensive traffic labeling, and user privacy concerns. The limited computing resources of 5G CPE further complicate efficient classification. Moreover, the "black box" nature of AI-TC models raises transparency and credibility issues. The paper proposes the FedEdge AI-TC framework, leveraging Federated Learning (FL) for reliable Network TC in 5G CPE. FL ensures privacy by employing local training, model parameter iteration, and centralized training. A semi-supervised TC algorithm based on Variational Auto-Encoder (VAE) and convolutional neural network (CNN) reduces data dependency while maintaining accuracy. To optimize model light-weight deployment, the paper introduces XAI-Pruning, an AI model compression method combined with DL model interpretability. Experimental evaluation demonstrates FedEdge AI-TC's superiority over benchmarks in terms of accuracy and efficient TC performance. The framework enhances user privacy and model credibility, offering a comprehensive solution for dependable and transparent Network TC in 5G CPE, thus enhancing service quality and security.