Federated learning has emerged recently as a promising solution for distributing machine
learning tasks through modern networks of mobile devices. Recent studies have obtained …

Federated edge learning (FEEL) has emerged as an effective approach to reduce the large
communication latency in Cloud-based machine learning solutions, while preserving data …

We consider federated learning (FL) with multiple wireless edge servers having their own
local coverage. We focus on speeding up training in this increasingly practical setup. Our …

With the growth of participating clients, the centralized parameter server (PS) will seriously
limit the scale and efficiency of Federated Learning (FL). A straightforward approach to scale …

Federated learning is a novel paradigm that involves learning from data samples distributed
across a large network of clients while the data remains local. It is, however, known that …

Federated learning (FL), in contrast to traditional centralized learning, has gained significant
attention as it enables the training of high-performance neural networks by maintaining user …

In Federated Learning (FL), clients may have weak devices that cannot train the full model or
even hold it in their memory space. To implement large-scale FL applications, thus, it is …

Federated Learning (FL) has been widely adopted to process the enormous data in the
application scenarios like Edge Computing (EC). However, the commonly-used …

Due to the limited resources and data privacy issue, last decade witnesses the fast
development of Distributed Machine Learning (DML) at network edges. Among all the …

Federated Learning (FL) is a distributed machine learning technology that trains models on
large-scale distributed devices while keeping training data localized and privatized …