Federated Deep Learning for Intrusion Detection in Consumer-Centric Internet of Things

Consumer-centric Internet of Things (CIoT) will play a pivotal role in the fifth industrial revolution (Industry 5.0) but it exhibits vulnerabilities that can render it susceptible to various cyberattacks. Recent studies have explored the potential of Federated Learning (FL) for privacy-preserving intrusion detection in IoT. However, the development of the FL models relied on unrealistic and irrelevant network traffic data, while also exhibiting limitations in terms of covered attack types and classification scenarios. In this paper, we develop Federated Deep Learning (FDL) models using three recent and highly relevant datasets, covering a wide range of attack types as well as binary and multi-class classification scenarios. Our findings demonstrate that the FDL models not only achieve high classification performance, comparable to traditional Centralized Deep Learning (CDL) models, in terms of accuracy
$(99.60\pm 0.46\%)$
, precision
$(92.50\pm 8.40\%)$
, recall
$(95.42\pm 6.24\%)$
, and F1 score
$(93.51\pm 7.76\%)$
but also exhibit superior computational efficiency compared to their CDL counterparts. The FDL approach reduces the training time by
$30.52 - 75.87\%$
. These classification performance and computational efficiency were achieved through multiple rounds of distributed local training in FDL. Therefore, the proposed FDL framework presents a robust security solution for designing and deploying a resilient CIoT.