The conventional federated learning (FedL) architecture distributes machine learning (ML)
across worker devices by having them train local models that are periodically aggregated by …

Federated learning (FL) enables on-device training over distributed networks consisting of a
massive amount of modern smart devices, such as smartphones and IoT (Internet of Things) …

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 Learning (FL) is a novel distributed machine learning which allows thousands of
edge devices to train model locally without uploading data concentrically to the server. But …

Federated learning (FL) is a distributed learning paradigm that enables a large number of
devices to collaboratively learn a model without sharing their raw data. Despite its practical …

Federated learning aims to jointly learn statistical models over massively distributed remote
devices. In this work, we propose FedDANE, an optimization method that we adapt from …

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 has emerged as a popular technique for distributing machine learning
(ML) model training across the wireless edge. In this paper, we propose two timescale …

There is an increasing interest in a new machine learning technique called Federated
Learning, in which the model training is distributed over mobile user equipments (UEs), and …

The widespread deployment of machine learning applications in ubiquitous environments
has sparked interests in exploiting the vast amount of data stored on mobile devices. To …