The Internet of Things (IoT) has experienced substantial growth in recent years, leading to a significant increase in the number of Internet-connected devices. This rapid expansion has raised concerns regarding the escalating frequency of cyber-attacks. So, it is of utmost importance to have an effective and reliable intrusion detection system (IDS) as part of a comprehensive defense strategy. Recent studies have demonstrated that the performance of IDS can be significantly improved by utilizing machine learning techniques. However, existing centralized techniques involve data sharing, which can increase the computational load and raise privacy concerns. In this paper, we utilize Federated Learning (FL), a distributed machine learning approach that minimizes data sharing and enhances privacy and performance. Additionally, an IDS requires a comprehensive and heterogeneous dataset with sufficient training data to achieve optimal performance. The scarcity of attack data creates an imbalance in the dataset, negatively impacting the model’s effectiveness. To address this problem, We use Generative Adversarial Networks (GANs) to augment the rare class data. Subsequently, We utilize the ANOVA feature selection method to down-sample the training dataset and obtain a rebalanced, low-dimensional dataset. In this article, we propose a novel approach to Intrusion Detection Systems (IDS) by combining Generative Adversarial Networks (GANs) and Federated Learning (FL). The proposed system, FLGAN-IDS, was experimentally evaluated using the NSL-KDD and CIC-IDS2017 datasets for binary and multi-class classification. The findings of this study demonstrate that the FLGAN-IDS model outperformed alternative methods in terms of accuracy, precision, and efficiency, exhibiting remarkable recall and F1-score metrics.