Transferring large models in federated learning (FL) networks is often hindered by clients'
limited bandwidth. We propose, an FL algorithm which achieves fast convergence by …

Federated learning (FL) has been widely used to train machine learning models over
massive data in edge computing. However, the existing FL solutions may cause long …

In federated learning, clients cooperatively train a global model by training local models over
their datasets under the coordination of a central server. However, clients may sometimes be …

Delayed Gradient Averaging (DGA) has gained massive attention for improving the training
efficiency of Federated Learning (FL) at edge networks, by allowing local computation in …

Federated learning (FL) is a distributed learning paradigm that enables a large number of
mobile devices to collaboratively learn a model under the coordination of a central server …

Federated learning (FL) is an emerging learning paradigm to tackle massively distributed
data. In Federated Learning, a set of clients jointly perform a machine learning task under …

Federated learning is a promising paradigm that allows multiple clients to collaboratively
train a model without sharing the local data. However, the presence of heterogeneous …

Federated learning is a promising approach for training models on distributed data, driven
by increasing demand in various industries. However, federated learning framework faces …

In Federated Learning (FL) client devices connected over the internet collaboratively train a
machine learning model without sharing their private data with a central server or with other …

Federated learning has received significant attention as a potential solution for distributing
machine learning (ML) model training through edge networks. This work addresses an …