From facial recognition to autonomous driving, Artificial Intelligence (AI) will transform the
way we live and work over the next couple of decades. Existing AI approaches for urban …

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

Federated learning (FL) is a kind of distributed machine learning framework, where the
global model is generated on the centralized aggregation server based on the parameters of …

The widespread deployment of machine learning applications in ubiquitous environments
has sparked interests in exploiting the vast amount of data stored on mobile devices. To …

With an increasing number of smart devices like internet of things devices deployed in the
field, offloading training of neural networks (NNs) to a central server becomes more and …

Edge Intelligence (EI) allows Artificial Intelligence (AI) applications to run at the edge, where
data analysis and decision-making can be performed in real-time and close to data sources …

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 …

As a new distributed machine learning (ML) framework for privacy protection, federated
learning (FL) enables substantial Internet of Things (IoT) devices (eg, mobile phones …

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 …

Nowadays, devices are equipped with advanced sensors with higher processing and
computing capabilities. Besides, widespread Internet availability enables communication …