The federated learning has gained prominent attention as a collaborative machine learning
method, allowing multiple users to jointly train a shared model without directly exchanging …

Federated Learning (FL) has emerged as a powerful approach to facilitate the construction
of centralized models without compromising the data privacy of multiple participants …

Federated Learning (FL) is a new computing paradigm in privacy-preserving Machine
Learning (ML), where the ML model is trained in a decentralized manner by the clients …

Abstract Machine learning (ML) plays a growing role in the Internet of Things (IoT)
applications and has efficiently contributed to many aspects, both for businesses and …

In today's world, the rapid expansion of IoT networks and the proliferation of smart devices in
our daily lives, have resulted in the generation of substantial amounts of heterogeneous …

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 …

Abstract The Internet-of-Things (IoT) generates vast quantities of data. Much of this data is
attributable to human activities and behavior. Collecting personal data and executing …

Federated learning (FL) represents an advanced approach to tackling the issues linked with
training machine learning (ML) models using distributed data while upholding privacy and …

Federated learning (FL) that enables edge devices to collaboratively learn a shared model
while keeping their training data locally has received great attention recently and can protect …

In the realm of federated learning (FL), the exchange of model data may inadvertently
expose sensitive information of participants, leading to significant privacy concerns. Existing …