Federated learning (FL) has recently become an effective approach for cyberattack detection
systems, especially in Internet of Things (IoT) networks. By distributing the learning process
across IoT gateways, FL can improve learning efficiency, reduce communication overheads,
and enhance privacy for cyberattack detection systems. However, one of the biggest
challenges for deploying FL in IoT networks is the unavailability of labeled data and
dissimilarity of data features for training. In this article, we propose a novel collaborative …

Federated Learning (FL) has recently become an effective approach for cyberattack
detection systems, especially in Internet-of-Things (IoT) networks. By distributing the
learning process across IoT gateways, FL can improve learning efficiency, reduce
communication overheads and enhance privacy for cyberattack detection systems.
Challenges in implementation of FL in such systems include unavailability of labeled data
and dissimilarity of data features in different IoT networks. In this paper, we propose a novel …