Artificial intelligence (AI) has witnessed a substantial breakthrough in a variety of Internet of
Things (IoT) applications and services, spanning from recommendation systems and speech …

Federated learning (FL) is a promising new technology that allows machine learning (ML)
models to be trained locally on edge devices while preserving the privacy of the devices' …

Federated Learning (FL) is a distributed learning methodology that allows multiple nodes to
cooperatively train a deep learning model, without the need to share their local data. It is a …

Distributed learning algorithms aim to leverage distributed and diverse data stored at users'
devices to learn a global phenomena by performing training amongst participating devices …

Massive map data transmission and the strict demand for the privacy of high-precision maps
have brought significant challenges to the cache of high-precision maps in intelligent …

In this article, the deployment of federated learning (FL) is investigated in an energy
harvesting wireless network in which the base stations (BSs) employs massive multiple …

Autonomous systems are becoming inherently ubiquitous with the advancements of
computing and communication solutions enabling low-latency offloading and real-time …

The combination of artificial intelligence and mobile edge computing (MEC) is considered as
a promising evolution path of the future wireless networks. As a model-level coordination …

Bearing fault diagnosis can be used to accurately and automatically identify the type and
severity of faults. Federation learning can perform learning without transferring local data …

In Federated Learning (FL), a global statistical model is developed by encouraging mobile
users to perform the model training on their local data and aggregating the output local …