Federated Learning (FL) is a privacy-preserving collaborative learning approach that trains
artificial intelligence (AI) models without revealing local datasets of the FL workers. While FL …

Federated learning (FL) has attracted much attention as a privacy-preserving distributed
machine learning framework, where many clients collaboratively train a machine learning …

Federated learning (FL) is a privacy-preserving distributed machine learning framework,
which involves training statistical models over a number of mobile users (ie, workers) while …

Federated Learning (FL) has recently received a lot of attention for large-scale privacy-
preserving machine learning. However, high communication overheads due to frequent …

Federated learning (FL) has prevailed as an efficient and privacy-preserved scheme for
distributed learning. In this work, we mainly focus on the optimization of computation and …

Federated learning (FL) is a swiftly evolving field within machine learning for collaboratively
training models at the network edge in a privacy-preserving fashion, without training data …

Federated learning (FL) is a promising privacy-preserving solution to build powerful AI
models. In many FL scenarios, such as healthcare or smart city monitoring, the user's …

Federated learning (FL) emerges as a promising paradigm to enable a federation of clients
to train a machine learning model in a privacy-preserving manner. Most existing works …

In federated learning (FL), a federation distributedly trains a collective machine learning
model by leveraging privacy preserving technologies. However, FL participants need to …

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 …