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As a typical entity of MEC (Mobile Edge Computing), 5G CPE (Customer PremiseEquipment)/HGU (Home Gateway Unit) has proven to be a promising alternative totraditional Smart Home Gateway. Network TC (Traffic Classification) is a vitalservice quality assurance and security management method for communicationnetworks, which has become a crucial functional entity in 5G CPE/HGU. In recentyears, many researchers have applied Machine Learning or Deep Learning (DL) toTC, namely AI-TC, to improve its performance. However, AI-TC faces challenges,including data dependency, resource-intensive traffic labeling, and userprivacy concerns. The limited computing resources of 5G CPE further complicateefficient classification. Moreover, the "black box" nature of AI-TC modelsraises transparency and credibility issues. The paper proposes the FedEdgeAI-TC framework, leveraging Federated Learning (FL) for reliable Network TC in5G CPE. FL ensures privacy by employing local training, model parameteriteration, and centralized training. A semi-supervised TC algorithm based onVariational Auto-Encoder (VAE) and convolutional neural network (CNN) reducesdata dependency while maintaining accuracy. To optimize model light-weightdeployment, the paper introduces XAI-Pruning, an AI model compression methodcombined with DL model interpretability. Experimental evaluation demonstratesFedEdge AI-TC's superiority over benchmarks in terms of accuracy and efficientTC performance. The framework enhances user privacy and model credibility,offering a comprehensive solution for dependable and transparent Network TC in5G CPE, thus enhancing service quality and security.

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