Federated learning has become the promising approach for building collaborative intrusion detection systems (IDS) as providing privacy guaranteeing among data holders. Nevertheless, the non-independent and identically distributed (Non-IID) data in real-world scenarios negatively impacts the performance of aggregated models from training client updates. To this end, in this paper, we introduce Generative Adversarial Networks (GANs) and Reinforcement Learning (RL) approach for federated IDS that can deal with Non-IID data among organizational networks. More specifically, the imbalanced state between data classes is tackled by GAN-based data augmentation, while RL provides better performance in the client choosing process for federated IDS model training. Finally, the experimental results on Kitsune dataset indicate that our work can help to set up the collaboration between data holders for building more effective IDS to deploy in practice with distinguished data distribution.
