  The evolution of cybersecurity is undoubtedly associated and intertwined with
the development and improvement of artificial intelligence (AI). As a key tool
for realizing more cybersecure ecosystems, Intrusion Detection Systems (IDSs)
have evolved tremendously in recent years by integrating machine learning (ML)
techniques for the detection of increasingly sophisticated cybersecurity
attacks hidden in big data. However, these approaches have traditionally been
based on centralized learning architectures, in which data from end nodes are
shared with data centers for analysis. Recently, the application of federated
learning (FL) in this context has attracted great interest to come up with
collaborative intrusion detection approaches where data does not need to be
shared. Due to the recent rise of this field, this work presents a complete,
contemporary taxonomy for FL-enabled IDS approaches that stems from a
comprehensive survey of the literature in the time span from 2018 to 2022.
Precisely, our discussion includes an analysis of the main ML models, datasets,
aggregation functions, as well as implementation libraries, which are employed
by the proposed FL-enabled IDS approaches. On top of everything else, we
provide a critical view of the current state of the research around this topic,
and describe the main challenges and future directions based on the analysis of
the literature and our own experience in this area.
