Federated Learning (FL) has been foundational in improving the performance of a wide
range of applications since it was first introduced by Google. Some of the most prominent …

Federated learning (FL) has been developed as a promising framework to leverage the
resources of edge devices, enhance customers' privacy, comply with regulations, and …

The federated learning technique (FL) supports the collaborative training of machine
learning and deep learning models for edge network optimization. Although a complex edge …

This paper provides a comprehensive study of Federated Learning (FL) with an emphasis
on enabling software and hardware platforms, protocols, real-life applications and use …

Driven by privacy concerns and the visions of deep learning, the last four years have
witnessed a paradigm shift in the applicability mechanism of machine learning (ML). An …

Federated learning (FL) refers to a system in which a central aggregator coordinates the
efforts of several clients to solve the issues of machine learning. This setting allows the …

Federated learning (FL) is a machine learning setting where many clients (eg, mobile
devices or whole organizations) collaboratively train a model under the orchestration of a …

Since its inception in 2016, federated learning has evolved into a highly promising decentral-
ized machine learning approach, facilitating collaborative model training across numerous …

Abstract In 2016, Google proposed Federated Learning (FL) as a novel paradigm to train
Machine Learning (ML) models across the participants of a federation while preserving data …

Federated learning (FL) is an emerging, privacy-preserving machine learning paradigm,
drawing tremendous attention in both academia and industry. A unique characteristic of FL …