In federated learning (FL), which clients are selected and which quantization levels are
chosen for the deep model parameters have significant impacts on the learning time as well …

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 fast-growing machine learning technique called
Federated Learning (FL), in which the model training is distributed over mobile user …

Federated learning (FL) enables mobile devices to collaboratively learn a shared prediction
model while keeping data locally. However, there are two major research challenges to …

Federated learning (FL), an emerging distributed machine learning paradigm, in conflux with
edge computing is a promising area with novel applications over mobile edge devices. In …

Edge federated learning (FL) is an emerging paradigm that trains a global parametric model
from distributed datasets based on wireless communications. This paper proposes a unit …

In this paper, we quantitatively compare these two effective communication schemes, ie,
digital and analog ones, for wireless federated learning (FL) over resource-constrained …

Federated learning (FL) is an emerging machine learning paradigm that allows to
accomplish model training without aggregating data at a central server. Most studies on FL …

Federated learning (FL) is a privacy-preserving distributed machine learning technique that
trains models while keeping all the original data generated on devices locally. Since devices …

Federated learning (FL) involves training a model over massive distributed devices, while
keeping the training data localized. This form of collaborative learning exposes new …