Federated edge learning (FEEL) is a popular distributed learning framework that allows
privacy-preserving collaborative model training via periodic learning-updates …

FEderated Edge Learning (FEEL) has emerged as a leading technique for privacy-
preserving distributed training in wireless edge networks, where edge devices …

Federated learning (FL) is a useful tool in distributed machine learning that utilizes users'
local datasets in a privacy-preserving manner. When deploying FL in a constrained wireless …

Owing to the increasing need for massive data analysis and model training at the network
edge, as well as the rising concerns about the data privacy, a new distributed training …

Federated edge learning (FEEL) is envisioned as a promising paradigm to achieve privacy-
preserving distributed learning. However, it consumes excessive learning time due to the …

Federated learning (FL) is a swiftly evolving field within machine learning for collaboratively
training models at the network edge in a privacy-preserving fashion, without training data …

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 …

Federated Learning (FL) has been proposed as an appealing approach to handle data
privacy issue of mobile devices compared to conventional machine learning at the remote …

To leverage massive distributed data and computation resources, machine learning in the
network edge is considered to be a promising technique, especially for large-scale model …

Federated Learning (FL) is a distributed machine learning technique, where each device
contributes to the learning model by independently computing the gradient based on its …