Due to the greatly improved capabilities of devices, massive data, and increasing concern
about data privacy, Federated Learning (FL) has been increasingly considered for …

Utilizing machine learning (ML)-based approaches for network intrusion detection systems
(NIDSs) raises valid concerns due to the inherent susceptibility of current ML models to …

Network intrusion detection methods based on federated learning (FL) and edge computing
have great potential for protecting the cybersecurity of the Internet of Things. It overcomes …

Federated Learning (FL) has been recently receiving increasing consideration from the
cybersecurity community as a way to collaboratively train deep learning models with …

Federated learning (FL) provides autonomy and privacy by design to participating peers,
who cooperatively build a machine learning (ML) model while keeping their private data in …

Intrusion detection is essential for safeguarding computer systems and networks against
unauthorized access, malicious activities, and security breaches. Its application domains …

This paper presents a novel approach to analyzing trends in federated learning (FL) using
automatic semantic keyword clustering. The authors collected a dataset of FL research …

Federated Learning (FL) has emerged as a promising decentralized paradigm for training
machine learning models across distributed devices, ushering in a new era of collaborative …

As a distributed machine learning paradigm, federated learning (FL) is collaboratively
carried out on privately owned datasets but without direct data access. Although the original …

As computer networks become increasingly important in various domains, the need for
secure and reliable networks becomes more pressing, particularly in the context of …