  Intrusion detection systems are evolving into intelligent systems that
perform data analysis searching for anomalies in their environment. The
development of deep learning technologies opened the door to build more complex
and effective threat detection models. However, training those models may be
computationally infeasible in most Internet of Things devices. Current
approaches rely on powerful centralized servers that receive data from all
their parties -- violating basic privacy constraints and substantially
affecting response times and operational costs due to the huge communication
overheads. To mitigate these issues, Federated Learning emerged as a promising
approach where different agents collaboratively train a shared model, neither
exposing training data to others nor requiring a compute-intensive centralized
infrastructure. This paper focuses on the application of Federated Learning
approaches in the field of Intrusion Detection. Both technologies are described
in detail and current scientific progress is reviewed and categorized. Finally,
the paper highlights the limitations present in recent works and presents some
future directions for this technology.
