GAIA-FL: Generative AI-Augmented Federated Learning for Intrusion Detection System

In recent years, federated learning (FL) has gained significant attention as a privacy-preserving solution for distributed machine learning, particularly in cybersecurity applications such as intrusion and attack detection. However, traditional FL models often face challenges related to limited training data diversity, communication overhead, and the ability to adapt to novel or unforeseen attack patterns. At the same time, generative AI (GAI) models have emerged as powerful tools for synthesizing realistic data, enabling enhanced model generalization and robustness. In this work, we propose integrating GAI with FL to create a more effective and adaptive framework for cybersecurity called GAIA-FL (GAI-Augmented FL). GAI can augment FL by synthesizing diverse attack scenarios, enriching local datasets, and addressing data heterogeneity across distributed nodes. We analyze the unique capabilities of GAI, such as data generation, and highlight its potential to improve the performance of FL-based cybersecurity systems. Additionally, we explore the integration of generative models and FL, focusing on their combined ability to detect complex and evolving threats while maintaining data privacy. Unlike existing studies, our work emphasizes the fusion of GAI and FL to tackle the challenges of decentralized intrusion detection and attack prevention. To validate our approach, we present a case study where GAI is used to enhance FL-based network intrusion detection by generating synthetic attack data, improving detection accuracy and robustness. This work demonstrates how this integration can revolutionize cybersecurity in next-generation networks by providing scalable, privacy-preserving, and adaptive solutions for evolving cyber threats.