With the development of wireless networks, the scale of network optimization problems is
growing correspondingly. While algorithms have been designed to reduce complexity in …

Recent times are witnessing rapid development in machine learning algorithm systems,
especially in reinforcement learning, natural language processing, computer and robot …

Abstract Recently, the Deep Learning community has become interested in evolutionary
optimization (EO) as a means to address hard optimization problems, eg meta-learning …

Just like electricity, Machine Learning will revolutionize our life in many ways–some of which
are not even conceivable today. This book provides a thorough conceptual understanding of …

Recently, unmanned aerial vehicles (UAVs) have gained notable interest in various
applications such as wireless coverage, aerial surveillance, precision agriculture …

In order to effectively provide ultra reliable low latency communications and pervasive
connectivity for Internet of Things (IoT) devices, next-generation wireless networks can …

Federated learning (FL) is an attractive paradigm for making use of rich distributed data
while protecting data privacy. Nonetheless, non-ideal communication links and limited …

Flying ad-hoc networks (FANET) are one of the most important branches of wireless ad-hoc
networks, consisting of multiple unmanned air vehicles (UAVs) performing assigned tasks …

Abstract Domain-specific modeling priors and specialized components are becoming
increasingly important to the machine learning field. These components integrate …

Federated learning (FL) is a framework for distributed learning of centralized models. In FL,
a set of edge devices train a model using their local data, while repeatedly exchanging their …