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

As part of vehicle to everything (V2X) environments, intelligent connected vehicles (ICVs)
generate a large amount of data, which can be exploited securely and effectively through …

Federated learning, as a novel distributed machine learning mode, enables the training of
machine learning models on multiple devices while ensuring data privacy. However, the …

The necessities of security and data sharing have focused on federated learning because of
using decentralized data sources. The existing works used federated learning for security …

In this paper, we propose a robust communication-efficient decentralized learning algorithm,
named RCEDL, to address data heterogeneity, communication heterogeneity and …

Federated Learning (FL) is a Machine Learning (ML) technique that aims to reduce the
threats to user data privacy. Training is done using the raw data on the users' devices, called …

Federated Learning (FL) has gained significant traction as a promising approach to enable
collaborative machine learning (ML) while safeguarding data privacy across diverse …

With the advancement of IoT technology, smart IoT systems have become integral to
industrial production and daily life. However, they face significant security and privacy …

Machine learning models can support decision-making in mobile terminals (MTs)
deployments, but their training generally requires massive datasets and abundant …

Federated learning (FL) has gained widespread popularity in recent years due to the fast
booming of advanced machine learning and artificial intelligence, along with emerging …