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

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 …

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

The proliferation of intelligence services brings data breaches and privacy infringement
concerns. To preserve data privacy when training machine learning models, the federated …

Classical federated learning methods aggregate decentralized data from different devices
into a central location for efficient training. However, these approaches raise significant …

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 …

Digital twin (DT) has emerged as a promising solution to enhance manufacturing efficiency
in industrial Internet of Things (IIoT) networks. To promote the efficiency and trustworthiness …