Federated learning (FL) is a machine learning (ML) paradigm that enables distributed agents to learn collaborative models without sharing data. In the context of network security, FL promises to improve the detection and mitigation of attacks, notably by virtually extending the local dataset of each participant. However, one of the major challenges of this recent technology is the heterogeneity of the data used by the participants. Indeed, some participants with very different monitoring contexts could penalize the global model. Furthermore, identifying malicious contributions is made more difficult in heterogeneous environments. In this tutorial, we will first present the fundamentals of federated learning, then focus on its use in network monitoring, and more specifically, in collaborative intrusion detection (Federated Learning-based Intrusion Detection System-FIDS). Secondly, we will address some of the open research questions in this context [LPBA22], before focusing on the problem of training data heterogeneity. Finally, we will discuss the security of FL architectures, and more specifically, the problem of poisoning attacks. All these parts will be illustrated by hands-on exercises, guided step by step throughout the tutorial.