Abstract The application of Machine Learning (ML) techniques to the well-known intrusion
detection systems (IDS) is key to cope with increasingly sophisticated cybersecurity attacks …

The Internet of Things (IoT) ecosystem connects physical devices to the internet, offering
significant advantages in agility, responsiveness, and potential environmental benefits. The …

In this article, we present a comprehensive study with an experimental analysis of federated
deep learning approaches for cyber security in the Internet of Things (IoT) applications …

Abstract Machine learning models based on sensitive data in the real-world promise
advances in areas ranging from medical screening to disease outbreaks, agriculture …

Federated learning has been applied to various tasks in intelligent transportation systems to
protect data privacy through decentralized training schemes. The majority of the state-of-the …

The federated learning technique (FL) supports the collaborative training of machine
learning and deep learning models for edge network optimization. Although a complex edge …

Research on smart connected vehicles has recently targeted the integration of vehicle-to-
everything (V2X) networks with Machine Learning (ML) tools and distributed decision …

The fast proliferation of edge computing devices brings an increasing growth of data, which
directly promotes machine learning (ML) technology development. However, privacy issues …

Conventional machine learning approaches aggregate all training data in a central server,
which causes massive communication overhead of data transmission and is also vulnerable …

Federated Learning (FL) is a privacy-preserving collaborative learning approach that trains
artificial intelligence (AI) models without revealing local datasets of the FL workers. While FL …