This survey aims to identify commonly used methods, datasets, future trends, knowledge
gaps, constraints, and limitations in the field to provide an overview of current solutions used …

Recent developments in deep learning have contributed to numerous success stories in
healthcare. The performance of a deep learning model generally improves with the size of …

The study considers the closed-loop supply chain (CLSC) decision using federated learning
platform (FL platform), establishes a CLSC game model including one manufacturer, one …

This systematic literature review scrutinizes the landscape of research at the intersection of
federated learning, big data processing, and data poisoning attacks. Employing a …

Digital therapeutics, evidence-based treatments delivered through software programs, are
revolutionizing healthcare by utilizing cutting-edge computing technologies. Unlike …

Several decades ago, the medicine supply chain (MSC) transferred the medicines from the
manufacturer to the end‐consumer and kept all records in a manual register. The manual …

With the rapid development of enabling technologies like VR and AR, we human beings are
on the threshold of the ubiquitous human-centric intelligence era. 6G is believed to be an …

Purpose For privacy protection, federated learning based on data separation allows
machine learning models to be trained on remote devices or in isolated data devices …

In a data-decentralized environment where data are distributed across individual local
nodes, inter-node data sharing is often restricted due to security concerns and …

Federated learning (FL) is considered a de facto standard for privacy preservation in AI
environments because it does not require data to be aggregated in some central place to …