In recent years, Federated Learning (FL) has gained relevance in training collaborative
models without sharing sensitive data. Since its birth, Centralized FL (CFL) has been the …

Recently, innovations in the Internet of Medical Things (IoMT), information and
communication technologies, and machine learning (ML) have enabled smart healthcare …

Federated learning (FL) has been proposed as an emerging paradigm to perform privacy-
preserving distributed machine learning in the Internet of Things (IoT). However, the …

In this paper, we study a new latency optimization problem for blockchain-based federated
learning (BFL) in multi-server edge computing. In this system model, distributed mobile …

Unmanned aerial vehicles (UAVs) extend the traditional ground-based Internet of Things
(IoT) into the air. UAV mobile edge computing (MEC) architectures have been proposed by …

In the digital economy, the security of Cyber-Physical Systems (CPSs) is paramount. Despite
the deployment of various Intrusion Detection Systems (IDSs) in industrial CPSs, the primary …

Federated learning may be subject to both global aggregation attacks and distributed
poisoning attacks. Blockchain technology along with incentive and penalty mechanisms …

As the usage of artificial intelligence (AI) grows within the field of cybersecurity, so too does
the demand for secure and decentralized AI systems to protect against potential cyber …

Abstract The rise of Decentralized Federated Learning (DFL) has enabled the training of
machine learning models across federated participants, fostering decentralized model …

There is a growing need for authentication methodology in virtual reality applications.
Current systems assume that the immersive experience technology is a collection of …