An edge intelligence-aided Internet-of-Things (IoT) network has been proposed to
accelerate the response of IoT services by deploying edge intelligence near IoT devices …

As data become increasingly abundant and diverse, their potential to fuel machine learning
models is increasingly vast. However, traditional centralized learning approaches, which …

To achieve portable and intelligent pork meat freshness detection, this study combined
olfactory imaging technology with the Internet of Things (IoT). By conducting reaction …

Robust machine learning (ML) models can be developed by leveraging large volumes of
data and distributing the computational tasks across numerous devices or servers …

Automated communication within heterogeneous connectivity of participating devices is an
exceptional idea that has allowed the world to take modernized network communication for …

In federated learning (FL), in contrast to centralized ML learning processes, ML models are
sent rather than the raw data. Therefore, FL is a decentralized and privacy-compliant …

Federated Learning (FL) enabled edge computing has been widely used in training complex
deep learning models by coordinating various heterogeneous resources in Industrial …

Federated edge learning (FEEL) is a novel paradigm that enables privacy-preserving and
distributed machine learning on end devices. However, FEEL faces challenges from …

Robust machine learning (ML) models can be developed by leveraging large volumes of
data and distributing the computational tasks across numerous devices or servers …