… to combine the learning architecture with the emerging network virtualization paradigms, …
, thus fully unleashing the potential of the learning approach. Compared to past and current …

… An intelligent edge concept for wireless communication is elaborated in [16]. The learning-…
Few-shot learning and meta-learning are newly christened paradigms in ML that enable …

… learning model communication paradigm based on multiple model compression, which
greatly exploits the redundancy among multiple deep learning … learning model communication …

… learning (DL) has been flourishing in order to enable machine intelligence (MI) capabilities
in wireless communications … of wireless communications networks, ranging from learning …

… paradigms. … learning algorithms can improve the performance of wireless networks. In
Section VI, we introduce some typical deep learning algorithms and their applications in wireless …

… learning (DML) techniques, such as federated learning, partitioned learning, and distributed
reinforcement learning, have been increasingly applied to wireless communications. This is …

… to require more effective and efficient wireless communications than ever before. For this …
IoT wireless communication paradigm. In this vision, IoT devices must be able to not only learn …

… The design paradigms of conventional wireless communication systems have to consider
the influence of various uncertain factors in hardware implementation, and compensate for …

… wireless edge networks. We present a detailed overview of several emerging distributed
learning paradigms, including federated learning, … -design of wireless communication and FD as …

… wireless communications. Currently, such effects are intractable to account for and are treated
as probabilistic factors. This article proposes a radically different approach, enabling deter…