Edge Computing (EC) is a new architecture that extends Cloud Computing (CC) services
closer to data sources. EC combined with Deep Learning (DL) is a promising technology …

In recent years, mobile devices are equipped with increasingly advanced sensing and
computing capabilities. Coupled with advancements in Deep Learning (DL), this opens up …

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 …

Federated Learning is a machine learning scheme in which a shared prediction model can
be collaboratively learned by a number of distributed nodes using their locally stored data. It …

In this article, we present federated analytics, a new distributed computing paradigm for data
analytics applications with privacy concerns. With the advances of sensing, communication …

With the proliferation of mobile computing and Internet of Things (IoT), massive mobile and
IoT devices are connected to the Internet. These devices are generating a huge amount of …

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) is a distributed machine learning strategy that generates a global
model by learning from multiple decentralized edge clients. FL enables on-device training …

Nowadays, there is an ever-increasing deployment of intelligent edge devices, such as
smartphones, wearable devices, and autonomous vehicles. It is enabled by the integration …

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 …