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.