In this article, we increase the availability and integration of devices in the learning process
to enhance the convergence of federated learning (FL) models. To address the issue of …

Federated learning (FL) is an emerging distributed machine learning (ML) paradigm with
enhanced privacy, aiming to achieve a" good" ML model for as many as participants while …

As a privacy-preserving paradigm for training machine learning (ML) models, federated
learning (FL) has received tremendous attention from both industry and academia. In a …

Federated learning (FL) is a promising technique for collaboratively training machine-
learning models on massively distributed clients data under privacy constraints. However …

Federated learning (FL) is a promising new technology that allows machine learning (ML)
models to be trained locally on edge devices while preserving the privacy of the devices' …

Federated learning (FL) is a machine learning approach that decentralizes data and its
processing by allowing clients to train intermediate models on their devices with locally …

This work presents an efficient data-centric client selection approach, named DICE, to
enable federated learning (FL) over distributed edge networks. Prior research focused on …

Federated Learning (FL) trains a machine learning model on distributed clients without
exposing individual data. Unlike centralized training that is usually based on carefully …

Federated Learning is a machine learning paradigm where a global model is trained in-situ
across a large number of distributed edge devices. While this technique avoids the cost of …

With the arising concerns of privacy within machine learning, federated learning (FL) was
invented in 2017, in which the clients, such as mobile devices, train a model and send the …