Lately there has been an increase in the number of Machine Learning (ML) and Artificial
Intelligence (AI) applications ranging from recommendation systems to face to speech …

Introducing blockchain into Federated Learning (FL) to build a trusted edge computing
environment for transmission and learning has attracted widespread attention as a new …

Mobile-edge computing (MEC) has been envisioned as a promising paradigm to handle the
massive volume of data generated from ubiquitous mobile devices for enabling intelligent …

The demand for intelligent industries and smart services based on big data is rising rapidly
with the increasing digitization and intelligence of the modern world. This survey …

Several recent advances in Federated Learning have made it possible for researchers to
train their models on private data present on contributing devices without compromising their …

Federal learning (FL) can realize a distributed training machine learning models in multiple
devices while protecting their data privacy, but some defect still exists such as single point …

The role of the Internet of Things (IoT) in the revolutionized society cannot be overlooked.
The IoT can leverage advanced machine learning (ML) algorithms for its applications …

With the proliferation of computationally powerful edge devices, edge computing has been
widely adopted for wide-ranging computational tasks. Among these, edge artificial …

Blockchain-enabled Federated Learning (BFL) enables model updates to be stored in
blockchain in a reliable manner. However, one problem is the increase of the training …

Cognitive computing, a revolutionary AI concept emulating human brain's reasoning
process, is progressively flourishing in the Industry 4.0 automation. With the advancement of …