Cameras in modern devices such as smartphones, satellites and medical equipment are
capable of capturing very high resolution images and videos. Such high-resolution data …

In the era of beyond 5G technology, it is expected that more and more applications can use
deep neural network (DNN) models for different purposes with minimum inference time …

Portable electronic systems allow the analysis and monitoring of continuous time signals,
such as human activity, integrating deep learning techniques with cloud computing, causing …

Transforming off-the-shelf deep neural network (DNN) models into dynamic multi-exit
architectures can achieve inference and transmission efficiency by fragmenting and …

The split computing approach, where the head and tail models are respectively distributed
between the IoT device and cloud, suffers from high network latency especially when the …

Data‐hunger is a persistent challenge in machine learning, particularly in the field of image
processing based on convolutional neural networks (CNNs). This study systematically …

In the conventional split computing approach based on the external computing node (eg,
cloud), Internet of Things (IoT) devices suffer from high network latency. In this paper, we …

Split computing is a promising approach to reduce the inference latency of deep neural
network (DNN) models. In this article, we propose a two-phase split computing framework …

In the resource-constrained IoT-edge environment, Split Federated (SplitFed) learning is
implemented to enhance training efficiency. This method involves each IoT device dividing …

In recent times, Split Federated Learning (SFL) is being proposed in the domain of Artificial
Intelligence (AI) & Machine Learning (ML), where AI/ML models are partitioned into two or …