  Federated learning (FL) is a decentralized learning technique that enables
participating devices to collaboratively build a shared Machine Leaning (ML) or
Deep Learning (DL) model without revealing their raw data to a third party. Due
to its privacy-preserving nature, FL has sparked widespread attention for
building Intrusion Detection Systems (IDS) within the realm of cybersecurity.
However, the data heterogeneity across participating domains and entities
presents significant challenges for the reliable implementation of an FL-based
IDS. In this paper, we propose an effective method called Statistical Averaging
(StatAvg) to alleviate non-independently and identically (non-iid) distributed
features across local clients' data in FL. In particular, StatAvg allows the FL
clients to share their individual data statistics with the server, which then
aggregates this information to produce global statistics. The latter are shared
with the clients and used for universal data normalisation. It is worth
mentioning that StatAvg can seamlessly integrate with any FL aggregation
strategy, as it occurs before the actual FL training process. The proposed
method is evaluated against baseline approaches using datasets for network and
host Artificial Intelligence (AI)-powered IDS. The experimental results
demonstrate the efficiency of StatAvg in mitigating non-iid feature
distributions across the FL clients compared to the baseline methods.
