With outstanding features, machine learning (ML) has become the backbone of numerous
applications in wireless networks. However, the conventional ML approaches face many …

Distributed machine learning (DML) techniques, such as federated learning, partitioned
learning, and distributed reinforcement learning, have been increasingly applied to wireless …

Machine learning (ML) tasks are becoming ubiquitous in today's network applications.
Federated learning has emerged recently as a technique for training ML models at the …

The marriage of wireless big data and machine learning techniques revolutionizes wireless
systems by introducing data-driven philosophy. However, the ever exploding data volume …

With outstanding features, Machine Learning (ML) has been the backbone of numerous
applications in wireless networks. However, the conventional ML approaches have been …

We study a distributed machine learning problem carried out by an edge server and multiple
agents in a wireless network. The objective is to minimize a global function that is a sum of …

Bringing the success of modern machine learning (ML) techniques to mobile devices can
enable many new services and businesses, but also poses significant technical and …

Machine learning (ML) has been widely applied to the upper layers of wireless
communication systems for various purposes, such as deployment of cognitive radio and …

Advanced technologies are required in future mobile wireless networks to support services
with highly diverse requirements in terms of high data rate and reliability, low latency, and …

Intelligent communication is gradually becoming a mainstream direction. As a major branch
of machine learning, deep learning (DL) has been applied in physical layer communications …