In the Internet of Things (IoT) domain, the development and efficiency of Intrusion Detection Systems (IDS) powered by machine learning (ML) have seen substantial growth. Especially, Federated Learning-based (FL-based) IDS have experienced notable growth, focusing on reducing data privacy violations and alleviating the communication and high-cost burdens associated with dataset collection. However, these approaches continue to face several challenges, particularly the presence of non-independent and identically distributed (Non-IID) data and a lack of labeled data on the client side, which remain significant concerns. Additionally, adversarial attacks also pose a significant concern for ML classification models in general, and particularly for ML-based IDS. To overcome these challenges, our paper proposes a semi-supervised federated learning approach for IDS, called SeFed-IDS, designed to mitigate the impact of limited labeled data. Additionally, we incorporate an autoencoder network alongside the Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD) to augment data, effectively addressing the challenges posed by Non-IID data and adversarial attacks. In this study, we carried out experiments to assess the effectiveness of our approach in various scenarios, including those involving Non-IID data and different data distribution patterns. These experiments are conducted on two real-world datasets, NF-UNSW-NB15 and NF-CSE-CIC-IDS2018. Furthermore, the results indicate that our approach outperforms the original FL approach when dealing with adversarial data.