Background Federated learning (FL) is a newly proposed machine-learning method that
uses a decentralized dataset. Since data transfer is not necessary for the learning process in …

Privacy-preserving federated learning, as one of the privacy-preserving computation
techniques, is a promising distributed and privacy-preserving machine learning (ML) …

As data are increasingly being stored in different silos and societies becoming more aware
of data privacy issues, the traditional centralized training of artificial intelligence (AI) models …

Federated Learning (FL) is a decentralized learning approach, meaning it learns from data
housed locally on devices such as tablets, cellular phones, and more, and does not collect …

Amid growing concerns on data privacy, Federated Learning (FL) has emerged as a
promising privacy preserving distributed machine learning paradigm. Given that the FL …

Federated Learning (FL) has been recognized as a privacy-preserving machine learning
(ML) technology that enables collaborative training and learning of a global ML model …

With the outbreak of COVID-19, people are experiencing increasing physical and mental
health issues. Therefore, personal daily healthcare and monitoring become vital for our …

Federated learning (FL) has recently been proposed as an emerging paradigm to build
machine learning models using distributed training datasets that are locally stored and …

Industrial Internet of Things (IIoT) lays a new paradigm for the concept of Industry 4.0 and
paves an insight for new industrial era. Nowadays smart machines and smart factories use …

In recent years, the global Internet of Medical Things (IoMT) industry has evolved at a
tremendous speed. Security and privacy are key concerns on the IoMT, owing to the huge …