In this paper, we propose a novel algorithm for energy-efficient, low-latency, accurate
inference at the wireless edge, in the context of 6G networks endowed with reconfigurable …

While high accuracy is of paramount importance for deep learning (DL) inference, serving
inference requests on time is equally critical but has not been carefully studied especially …

The concept of hierarchical federated edge learning (H-FEEL) has been recently proposed
as an enhancement of federated learning model. Such a system generally consists of three …

Edge machine learning involves the deployment of learning algorithms at the wireless
network edge so as to leverage massive mobile data for enabling intelligent applications …

Future machine learning (ML) powered applications, such as autonomous driving and
augmented reality, involve training and inference tasks with timeliness requirements and are …

The recent revival of AI is revolutionizing almost every branch of science and technology.
Given the ubiquitous smart mobile gadgets and IoT devices, it is expected that a majority of …

Next-generation wireless networks must support ultra-reliable, low-latency communication
and intelligently manage a massive number of Internet of Things (IoT) devices in real-time …

In Federated edge learning (FEEL), energy-constrained devices at the network edge
consume significant energy when training and uploading their local machine learning …

Machine learning (ML) is a promising enabler for the fifth-generation (5G) communication
systems and beyond. By imbuing intelligence into the network edge, edge nodes can …

ML has been increasingly adopted in wireless communications, with popular techniques,
such as supervised, unsupervised, and reinforcement learning, applied to traffic …