The ever-growing data privacy concerns have transformed machine learning (ML)
architectures from centralized to distributed, leading to federated learning (FL) and split …

The increasingly deeper neural networks hinder the democratization of privacy-enhancing
distributed learning, such as federated learning (FL), to resource-constrained devices. To …

Motivated by ever-increasing computational resources at edge devices and increasing
privacy concerns, a new machine learning (ML) framework called federated learning (FL) …

Abstract Machine learning (ML) and Deep learning (DL) models are popular in many areas,
from business, medicine, industries, healthcare, transportation, smart cities, and many more …

With the rapid increase in the number of Internet of Things (IoT) devices and the amount of
data they generate, the traditional cloud-based approach is gradually unable to meet the …

The next-generation of wireless networks will enable many machine learning (ML) tools and
applications to efficiently analyze various types of data collected by edge devices for …

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 …

As increasing concerns have arisen on privacy leakage in data-driven smart services,
federated learning (FL) has been introduced to collaboratively learn an efficient model …

The explosive growth of smart devices (eg, mobile phones, vehicles, drones) with sensing,
communication, and computation capabilities gives rise to an unprecedented amount of …

Federated learning (FL) is a privacy-preserving learning framework, which collaboratively
learns a centralized model across edge devices. Each device trains an independent model …