Federated learning (FL) is a popular framework for training an AI model using distributed
mobile data in a wireless network. It features data parallelism by distributing the learning …

To leverage massive distributed data and computation resources, machine learning in the
network edge is considered to be a promising technique, especially for large-scale model …

Recent advancements in deep neural networks (DNN) enabled various mobile deep
learning applications. However, it is technically challenging to locally train a DNN model due …

This article investigates the analog gradient aggregation (AGA) solution to overcome the
communication bottleneck for wireless federated learning applications by exploiting the idea …

Owing to the increasing need for massive data analysis and model training at the network
edge, as well as the rising concerns about the data privacy, a new distributed training …

Federated learning (FL) enables edge devices, such as Internet of Things devices (eg,
sensors), servers, and institutions (eg, hospitals), to collaboratively train a machine learning …

Federated learning becomes increasingly attractive in the areas of wireless communications
and machine learning due to its powerful learning ability and potential applications. In …

There is an increasing interest in a new machine learning technique called Federated
Learning, in which the model training is distributed over mobile user equipments (UEs), and …

Modern mobile devices have access to a wealth of data suitable for learning models, which
in turn can greatly improve the user experience on the device. For example, language …

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 …