Abstract The Three Gorges Dam's operation has been recognized as a contributing factor to
slope instability and the reactivation of pre-existing deep-seated landslides in the region …

Machine learning typically relies on the assumption that training and testing distributions are
identical and that data is centrally stored for training and testing. However, in real-world …

In the Industrial Internet of Things (IIoT), cross-silo federated learning (CSFL) enables
entities, such as manufacturers and suppliers to train global models for optimizing …

Abstract Mobile Edge Computing (MEC) and Federated Learning (FL) have recently
attracted considerable interest for their potential applications across diverse domains. MEC …

Recent advancements in the Internet of Medical Things (IoMT) have significantly influenced
the development of smart healthcare systems. Mobile edge computing (MEC)-assisted …

Federated learning (FL) is decentralized machine learning framework that finds various
applications in health, finance, and the Internet of things. This article studies the under …

Federated Learning (FL) is a typical decentralized Machine Learning framework in which
clients invest resources to train their local models without sharing their data and then …

The evaluation of participant contribution in federated learning (FL) has recently gained
significant attention due to its applicability in various domains, such as incentive …

In cross-silo federated learning (FL), companies collaboratively train a shared global model
without sharing heterogeneous data. Prior related work focused on algorithm development …

Federated learning stands out as a promising approach within the domain of edge
computing, providing a framework for collaborative training on distributed datasets without …