Abstract The Internet of Things (IoT) has embedded itself in our daily lives, offering smart
services and AI-driven applications. However, traditional AI methods face challenges due to …

Abstract Recently, Federated Learning (FL) has been explored as a new paradigm that
preserves both data privacy and end-users knowledge while reducing latency during model …

In the realm of machine learning (ML) systems featuring client-host connections, the
enhancement of privacy security can be effectively achieved through federated learning (FL) …

Billions of IoT devices will be deployed in the near future, taking advantage of faster Internet
speed and the possibility of orders of magnitude more endpoints brought by 5G/6G. With the …

The proliferation of Internet of Things (IoT) devices has ushered in an era of unprecedented
data generation, necessitating innovative approaches to extract valuable insights while …

The proliferation of the Internet of Things (IoT) and the advancements in machine learning
(ML) have facilitated ubiquitous sensing and computing capabilities, enabling the …

The proliferation of IoT devices requires innovative approaches to gaining insights while
preserving privacy and resources amid unprecedented data generation. However, FL …

Federated learning (FL) is a new technology that has been a hot research topic. It enables
the training of an algorithm across multiple decentralized edge devices or servers holding …

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 …

Big data has remarkably evolved over the last few years to realize an enormous volume of
data generated from newly emerging services and applications and a massive number of …