5G brings significant advancement, offering lower latency, and improved connectivity. Yet, its complexity, stemming from factors such as integrating advanced technologies like Software Defined Networking (SDN) and slicing, introduces challenges in implementing strong security measures against emerging threats. Although Intrusion Detection Systems (IDSs) can successfully detect attacks, the novelty of 5G creates an expanded attack surface. Collaboration is essential for detecting novel, distributed attacks, and ensuring comprehensive observability in multiparty networks. However, such collaboration raises privacy concerns due to the sensitivity of shared data. Federated Learning (FL), a collaborative Machine Learning (ML) approach, is a promising solution to preserve privacy as the model is trained locally without exchanging raw data. In this paper, we examine ongoing efforts on FL-based IDS solutions in 5G. We set out to systematically review them in the light of challenges raised by their practical deployment in 5G networks. Out of the numerous papers we analyzed in FL, only 17 specifically concentrate on 5G scenarios making them the focus of this study. Towards systematizing knowledge, we first identify IDS challenges in 5G. Second, we classify FL-based IDS according to (i) their 5G application domain, (ii) 5G challenges they address, and (iii) their FL approach in terms of architecture, parameters, detection method, evaluation, etc. Through this examination, we find out that some issues receive less attention, prompting us to explore potential solutions. Additionally, we have identified other challenges, like the lack of evaluation results applicability due to the difficulties in getting high quality 5G datasets for evaluation.