As AI models expand in size, it has become increasingly challenging to deploy federated
learning (FL) on resource-constrained edge devices. To tackle this issue, split federated …

Federated edge learning (FEEL) provides a promising device‐edge collaborative learning
paradigm, which enables edge devices to parallel participate in model co‐creation while …

Human Activity Recognition (HAR) is an attractive topic to perceive human behavior and
supplying assistive services. Besides the classical inertial unit and vision-based HAR …

Edge learning (EL) is an end-edge collaborative learning paradigm that enables numerous
edge devices to participate in model training and data analysis, opening countless …

Deep neural network (DNN) is a promising technology for Internet of Visual Things (IoVT)
devices to extrct their visual information from unstructured data. However, it is hard to deploy …

Federated Learning (FL) has become a popular distributed machine learning technique that
preserves privacy of data set generated by the Internet of Things (IoT) devices. However …

Split learning (SL) has been recently proposed as a way to enable resource-constrained
devices to train multi-parameter neural networks (NNs) and participate in federated learning …

Abstract Resource Scheduling and Distributed learning play a key role in Internet of Things
(IoT) edge computing systems. There has been extensive research in each area, however …

Efficient on-device Convolutional Neural Network (CNN) training in resource-constrained
mobile and edge environments is an open challenge. Backpropagation is the standard …

The emerging load prediction techniques support up-front and rational resource
provisioning in edge systems to enhance system efficiency and Quality-of-Service (QoS) …