As the computing paradigm shifts from cloud computing to end-edge-cloud computing, it
also supports artificial intelligence evolving from a centralized manner to a distributed one …

Full leverage of the huge volume of data generated on a large number of user devices for
providing intelligent services in the 6G network calls for Ubiquitous Intelligence (UI). A key to …

Federated Learning (FL) is a collaborative machine learning (ML) framework that combines
on-device training and server-based aggregation to train a common ML model among …

Semantic communication has recently attracted significant interest from both industry and
academia due to its potential to transform the existing data-focused communication …

Federated learning (FL) has been widely acknowledged as a promising solution to training
machine learning (ML) model training with privacy preservation. To reduce the traffic …

With the advent of the Internet of things (IoT) era, federated learning plays an important role
in breaking through traditional data barriers and effectively realizing data privacy and …

To unveil the hidden value in the datasets of user equipments (UEs) while preserving user
privacy, federated learning (FL) is emerging as a promising technique to train a machine …

The cloud-based solutions are becoming inefficient due to considerably large time delays,
high power consumption, and security and privacy concerns caused by billions of connected …

Federated Learning (FL) is a new distributed machine learning (ML) approach which
enables thousands of mobile devices to collaboratively train artificial intelligence (AI) models …

Federated learning (FL), a novel distributed machine learning (DML) approach, has been
widely adopted to train deep neural networks (DNNs), over massive data in edge computing …