With the increasing development of 5G/Beyond 5G and network softwarization techniques, we have more flexibility and agility in the network. This can be exploited by Machine Learning (ML) to integrate intelligence in the network and improve network as well as service management in edge-cloud environment. Intrusion detection systems (IDS) is one of the challenging issues for managing network. However, traditional approaches in this domain require all data (and their associated labels) to be centralized at the same location. In this context, such approaches lead to: (i) a large bandwidth overhead, as raw data needs to be transmitted to the server, (ii) low incentives for devices to send their private data, and (iii) large computing and storage resources needed on the server side to label and treat all this data. In this paper, to cope with the above limitations, we propose a semi-supervised federated learning model for IDS. Moreover, we use network softwarisation for automation and deployment. Our model combines Federated Learning and Semi-Supervised Learning where the clients train unsupervised models (using unlabeled data) to learn the representative and low-dimensional features and the server conducts a supervised model (using labeled data). We evaluate this approach on the well-known UNSW-NB15 dataset and the experimental results demonstrate that our approach can achieve accuracy and detection rates up to 84.32% and 83.10%, respectively while keeping the data private with limited overhead.