This study develops a federated learning (FL) framework overcoming largely incremental
communication costs due to model sizes in typical frameworks without compromising model …

To mitigate the privacy leakages and communication burdens of Federated Learning (FL),
decentralized FL (DFL) discards the central server and each client only communicates with …

Federated Learning (FL) is a method of training machine learning models on private data
distributed over a large number of possibly heterogeneous clients such as mobile phones …

Federated learning (FL) has recently attracted increasing attention from academia and
industry, with the ultimate goal of achieving collaborative training under privacy and …

Federated Learning (FL) is an emerging distributed learning paradigm under privacy
constraint. Data heterogeneity is one of the main challenges in FL, which results in slow …

Federated learning (FL) is an emerging machine learning paradigm in which a central
server coordinates multiple participants (aka FL clients) to train a model collaboratively on …

Federated learning (FL) is a distributed deep learning method that enables multiple
participants, such as mobile and IoT devices, to contribute a neural network while their …

Federated learning has received increasing attention for its ability to collaborative learning
without leaking privacy. Promising advances have been achieved under the assumption that …

Federated learning (FL) enables resource-constrained edge nodes to collaboratively learn a
global model under the orchestration of a central server while keeping privacy-sensitive data …

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 …