Federated Traffic Synthesizing and Classification Using Generative Adversarial Networks

04/21/2021
by   Chenxin Xu, et al.
0

With the fast growing demand on new services and applications as well as the increasing awareness of data protection, traditional centralized traffic classification approaches are facing unprecedented challenges. This paper introduces a novel framework, Federated Generative Adversarial Networks and Automatic Classification (FGAN-AC), which integrates decentralized data synthesizing with traffic classification. FGAN-AC is able to synthesize and classify multiple types of service data traffic from decentralized local datasets without requiring a large volume of manually labeled dataset or causing any data leakage. Two types of data synthesizing approaches have been proposed and compared: computation-efficient FGAN (FGAN-1) and communication-efficient FGAN (FGAN-2). The former only implements a single CNN model for processing each local dataset and the later only requires coordination of intermediate model training parameters. An automatic data classification and model updating framework has been proposed to automatically identify unknown traffic from the synthesized data samples and create new pseudo-labels for model training. Numerical results show that our proposed framework has the ability to synthesize highly mixed service data traffic and can significantly improve the traffic classification performance compared to existing solutions.

READ FULL TEXT
research
02/01/2023

Distributed Traffic Synthesis and Classification in Edge Networks: A Federated Self-supervised Learning Approach

With the rising demand for wireless services and increased awareness of ...
research
07/27/2019

Generative Adversarial Network for Handwritten Text

Generative adversarial networks (GANs) has proven hugely successful in v...
research
11/28/2017

Differential Generative Adversarial Networks: Synthesizing Non-linear Facial Variations with Limited Number of Training Data

In face-related applications with a public available dataset, synthesizi...
research
08/18/2021

Fed-TGAN: Federated Learning Framework for Synthesizing Tabular Data

Generative Adversarial Networks (GANs) are typically trained to synthesi...
research
07/08/2021

A Federated Semi-Supervised Learning Approach for Network Traffic Classification

Network traffic classification, a task to classify network traffic and i...
research
12/27/2022

Co-supervised learning paradigm with conditional generative adversarial networks for sample-efficient classification

Classification using supervised learning requires annotating a large amo...
research
11/21/2019

Synthesizing Visual Illusions Using Generative Adversarial Networks

Visual illusions are a very useful tool for vision scientists, because t...

Please sign up or login with your details

Forgot password? Click here to reset