Due to the distributed collaboration and privacy protection features, federated learning is a
promising technology to perform the model training in virtual twins of Digital Twin for Mobile …

Aiming at protecting device data privacy, Federated Learning (FL) is a framework of
distributed machine learning in which devices' local model parameters are exchanged with …

Federated learning is defined as one of the solutions that use distributed clients to train and
aggregate the target model without sharing the private information of clients in the network …

Federated learning (FL) is a technique that allows multiple clients to collaboratively train a
global model without sharing their sensitive and bandwidth-hungry data. This paper …

The edge Industrial Internet of Things (IIoT) is highly vulnerable to attacks due to the vast
number of connected devices and the lack of security features. Attacks in edge IIoT can lead …

With the advancement and widespread adoption of Artificial Intelligence (AI) and Internet of
Things (IoT) technology, machine learning (ML) can be leveraged in au-tomated factories to …

Federated Learning (FL) has emerged to leverage datasets from multiple devices to improve
the performance of a Machine Learning (ML) model while providing privacy preservation for …