Federated learning (FL) is a distributed machine learning (ML) approach that enables
models to be trained on client devices while ensuring the privacy of user data. Model …

A revolution in advanced manufacturing has been driven by digital technology in the fourth
industrial revolution, also known as Industry 4.0, and has resulted in a substantial increase …

Federated learning of deep neural networks has emerged as an evolving paradigm for
distributed machine learning, gaining widespread attention due to its ability to update …

In order to describe the roadmap of current edge computing research activities, we first
address a brief overview of the most advanced edge computing surveys published in the last …

Collaborative edge computing (CEC) is an emerging paradigm enabling sharing of the
coupled data, computation, and networking resources among heterogeneous geo …

Edge computing has become a popular paradigm where services and applications are
deployed at the network edge closer to the data sources. It provides applications with …

Federated Learning (FL) has been widely used for distributed machine learning in edge
computing. In FL, the model parameters are iteratively aggregated from the clients to a …

Federated learning (FL) enables a large number of edge devices to learn a shared model
without data sharing collaboratively. However, the imbalanced data distribution among …

Distributed Machine Learning (DML) at the edge has become an essential topic for
providing low-latency intelligence near the data sources. However, both the development …

The advancement of mobile internet technology has created opportunities for integrating the
Industrial Internet of Things (IIoT) and edge computing in smart manufacturing. These …