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) has been considered as an appealing framework to tackle data
privacy issues of mobile devices compared to conventional Machine Learning (ML). Using …

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 (FL), arising as a privacy-preserving machine learning paradigm, has
received notable attention from the public. In each round of synchronous FL training, only a …

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) framework that
operates under privacy and communication constraints. To mitigate the data heterogeneity …

Federated learning (FL) is an emerging distributed and privacy-preserving machine learning
framework. However, the performance of traditional FL methods is seriously impaired by the …

Federated Learning (FL) enables distributed training by learners using local data, thereby
enhancing privacy and reducing communication. However, it presents numerous challenges …

Emerging machine learning (ML) technologies, in combination with the increasing
computational power of mobile devices, lead to the extensive adoption of ML-based …