Over the past few years, significant advancements have been made in the field of machine
learning (ML) to address resource management, interference management, autonomy, and …

The idea of federated learning is given by Google in 2016, to develop local-to-global
machine learning models by using data locally. Federated learning is a machine learning …

In today's world, the importance of the Green Internet of Things (GIoT) in the transformed
sustainable smart cities cannot be overstated. For a variety of applications, the GIoT may …

Federated learning (FL) has been proposed as a privacy-preserving distributed learning
paradigm, which differs from traditional distributed learning in two main aspects: the systems …

At present, the centralized learning models, used for IoT applications generating large
amount of data, face several challenges such as bandwidth scarcity, more energy …

Despite the evolution of distributed machine learning (ML) systems in recent years, the
communication overhead induced by their data transfers remains a major issue that …

This paper investigates the impact of communication factors on the convergence
performance of federated learning (FL) in wireless networks. Considering the limited …

Federated learning (FL) allows machine learning algorithms to gain insights into a broad
range of datasets located at different locations, enabling a privacy-preserving model …

Federated learning is a new distributed machine learning technology, where training tasks
are deployed on user side and training model parameters are sent to the server side. In the …

Over the last 75 years, artificial intelligence has evolved from a theoretical concept and
novel paradigm describing the role that computers might play in our society to a tool with …