  Critical role of Internet of Things (IoT) in various domains like smart city,
healthcare, supply chain and transportation has made them the target of
malicious attacks. Past works in this area focused on centralized Intrusion
Detection System (IDS), assuming the existence of a central entity to perform
data analysis and identify threats. However, such IDS may not always be
feasible, mainly due to spread of data across multiple sources and gathering at
central node can be costly. Also, the earlier works primarily focused on
improving True Positive Rate (TPR) and ignored the False Positive Rate (FPR),
which is also essential to avoid unnecessary downtime of the systems. In this
paper, we first present an architecture for IDS based on hybrid ensemble model,
named PHEC, which gives improved performance compared to state-of-the-art
architectures. We then adapt this model to a federated learning framework that
performs local training and aggregates only the model parameters. Next, we
propose Noise-Tolerant PHEC in centralized and federated settings to address
the label-noise problem. The proposed idea uses classifiers using weighted
convex surrogate loss functions. Natural robustness of KNN classifier towards
noisy data is also used in the proposed architecture. Experimental results on
four benchmark datasets drawn from various security attacks show that our model
achieves high TPR while keeping FPR low on noisy and clean data. Further, they
also demonstrate that the hybrid ensemble models achieve performance in
federated settings close to that of the centralized settings.
