Federated Edge Learning (FEEL) involves the collaborative training of machine learning
models among edge devices, with the orchestration of a server in a wireless edge network …

We introduce a new and increasingly relevant setting for distributed optimization in machine
learning, where the data defining the optimization are unevenly distributed over an …

Federated learning is a distributed machine learning technology that can protect users' data
privacy, so it has attracted more and more attention in the industry and academia …

Federated Learning is a distributed learning paradigm with two key challenges that
differentiate it from traditional distributed optimization:(1) significant variability in terms of the …

Federated learning (FL) has recently emerged as an important and promising learning
scheme in IoT, enabling devices to jointly learn a model without sharing their raw data sets …

We propose a novel federated learning method for distributively training neural network
models, where the server orchestrates cooperation between a subset of randomly chosen …

The aim of this paper is to propose a novel dynamic resource allocation strategy for energy-
efficient adaptive federated learning at the wireless network edge, with latency and learning …

The emerging concern about data privacy and security has motivated the proposal of
federated learning. Federated learning allows computing nodes to only synchronize the …

We study federated learning (FL), where power-limited wireless devices utilize their local
datasets to collaboratively train a global model with the help of a remote parameter server …

The dramatic success of deep learning is largely due to the availability of data. Data
samples are often acquired on edge devices, such as smartphones, vehicles, and sensors …