  The application of Machine Learning (ML) techniques to the well-known
intrusion detection systems (IDS) is key to cope with increasingly
sophisticated cybersecurity attacks through an effective and efficient
detection process. In the context of the Internet of Things (IoT), most
ML-enabled IDS approaches use centralized approaches where IoT devices share
their data with data centers for further analysis. To mitigate privacy concerns
associated with centralized approaches, in recent years the use of Federated
Learning (FL) has attracted a significant interest in different sectors,
including healthcare and transport systems. However, the development of
FL-enabled IDS for IoT is in its infancy, and still requires research efforts
from various areas, in order to identify the main challenges for the deployment
in real-world scenarios. In this direction, our work evaluates a FL-enabled IDS
approach based on a multiclass classifier considering different data
distributions for the detection of different attacks in an IoT scenario. In
particular, we use three different settings that are obtained by partitioning
the recent ToN\_IoT dataset according to IoT devices' IP address and types of
attack. Furthermore, we evaluate the impact of different aggregation functions
according to such setting by using the recent IBMFL framework as FL
implementation. Additionally, we identify a set of challenges and future
directions based on the existing literature and the analysis of our evaluation
results.
