Federated learning (FL) has received considerable attention with the development of mobile
internet technology, which is an emerging framework to train a deep learning model from …

Federated learning (FL) has become de facto framework for collaborative learning among
edge devices with privacy concern. The core of the FL strategy is the use of stochastic …

Secure model aggregation is a key component of federated learning (FL) that aims at
protecting the privacy of each user's individual model while allowing for their global …

In recent years, wireless federated learning (FL) has been proposed to support the mobile
intelligent applications over the wireless network, which protects the data privacy and …

The traditional cloud-centric approach for Deep Learning (DL) requires training data to be
collected and processed at a central server which is often challenging in privacy-sensitive …

We consider the scenario of inference at the wireless edge, in which devices are connected
to an edge server and ask the server to carry out remote classification, that is, classify data …

The use of unmanned aerial vehicles (UAVs) as flying users provides various applications
by exploiting machine learning (ML) algorithms. Recently, distributed learning algorithms …

Split learning is a promising privacy-preserving distributed learning scheme that has low
computation requirement at the edge device but has the disadvantage of high …

Federated meta‐learning (FML) has emerged as a promising paradigm to cope with the data
limitation and heterogeneity challenges in today's edge learning arena. However, its …

Mobile edge learning is an emerging technique that enables distributed edge devices to
collaborate in training shared machine learning (ML) models by exploiting their local data …