Federated learning (FL) is a scheme in which several consumers work collectively to unravel
machine learning (ML) problems, with a dominant collector synchronizing the procedure …

Sports involve commonly players and equipment of high dynamics. Their location and
motion data are essential for sports digitalization-related applications, such as from …

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 …

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 …

In this study, a weighted federated learning approach is proposed for electrocardiogram
(ECG) arrhythmia classification. The proposed approach considers the heterogeneity of data …

In response to various privacy risks, researchers and practitioners have been exploring
different paradigms that can leverage the increased computational capabilities of consumer …

Federated learning (FL) brings collaborative intelligence into industries without centralized
training data to accelerate the process of Industry 4.0 on the edge computing level. FL …

Federated learning (FL) is an emerging setting which implement machine learning in a
distributed environment while protecting privacy. Research activities relating to FLhave …

Federated learning (FL) is a collaborative, decentralized privacy‐preserving method to
attach the challenges of storing data and data privacy. Artificial intelligence, machine …

As a decentralized training approach, horizontal federated learning (HFL) enables
distributed clients to collaboratively learn a machine learning model while keeping …