The next-generation of wireless networks will enable many machine learning (ML) tools and
applications to efficiently analyze various types of data collected by edge devices for …

Standard machine-learning approaches involve the centralization of training data in a data
center, where centralized machine-learning algorithms can be applied for data analysis and …

The thriving of artificial intelligence (AI) applications is driving the further evolution of
wireless networks. It has been envisioned that 6G will be transformative and will …

In this paper, we investigate a distributed machine learning approach for a multiuser mobile
edge computing (MEC) network in a cognitive eavesdropping environment, where multiple …

In this paper, we study a relay-assisted federated edge learning (FEEL) network under
latency and bandwidth constraints. In this network, users collaboratively train a global model …

In this paper, we investigate how to deploy computational intelligence and deep learning
(DL) in edge-enabled industrial IoT networks. In this system, the IoT devices can …

The dramatic success of deep learning is largely due to the availability of data. Data
samples are often acquired on edge devices, such as smartphones, vehicles, and sensors …

Federated learning provides a promising paradigm to enable network edge intelligence in
the future sixth generation (6G) systems. However, due to the high dynamics of wireless …

The sixth generation (6G) wireless systems are envisioned to enable the paradigm shift from
“connected things” to “connected intelligence”, featured by ultra high density, large-scale …

Federated learning (FL) has been recognized as a viable distributed learning paradigm
which trains a machine learning model collaboratively with massive mobile devices in the …