Collaborative Federated Learning to Secure 6G-IoT with Deep Convolutional Generative Adversarial Network

Abstract

Wireless communication developments have significantly improved communications with each generation. The upcoming sixth-generation (6G) cellular wireless standard has raised expectations, particularly for Internet of Things (IoT) applications. 6G-based IoT (6G-IoT) aims to create an intelligent, ubiquitous, and self-optimizing IoT landscape. However, 6G-IoT faces security risks, and defending against attacks is increasingly challenging because of IoT devices’ distribution and heterogeneity. Most Federated Learning (FL)-based intrusion detection systems (IDSs) aim to enhance security, offering high reliability than local training. However, traditional FL suffers from high communication latency. FL over fog infrastructure is one solution. However, fog servers may not be available at every location, or communication with IoT devices may delay the learning process. This paper proposes a distributed IDS-based collaborative FL (IDS-CFL) across different learning levels: device and fog-cloud, to reduce data transfer. Neighboring devices at the device level collaborate, leveraging their computing capabilities for faster detection. To improve accuracy and enable fast processing, we propose a Deep Convolutional Generative Adversarial Network (DCGAN) model to train data at each level. The performance is evaluated on a recent dataset, Edge-IIoTest, and compared with other distributed and centralized methods. The results show the proposed system’s effectiveness, with 96.20% accuracy and 4.5 ms detection time.