Edge federated learning (FL) is an emerging paradigm that trains a global parametric model
from distributed datasets based on wireless communications. This paper proposes a unit …

Artificial Intelligence (Al) models are being produced and used to solve a variety of current
and future business and technical problems. Therefore, AI model engineering processes …

Edge computing is a new paradigm enabling intelligent applications for the Internet of
Things (IoT) using mobile, low-cost IoT devices embedded with data analytics. Due to the …

Different from the existing works that focus on transceiver design of over-the-air computation
(AirComp) over static networks, we in this paper consider an unmanned aerial vehicle (UAV) …

Autonomous driving services depends on active sensing from modules such as camera,
LiDAR, radar, and communication units. Traditionally, these modules process the sensed …

This paper develops a class of low-complexity device scheduling algorithms for over-the-air
federated learning via the method of matching pursuit. The proposed scheme tracks closely …

With low-cost energy consumption, the reconfigurable intelligent surface (RIS) technique is a
potential solution to the real-time data processing for intelligent transportation systems …

Leveraging recent advances on mobile edge computing (MEC), edge intelligence has
emerged as a promising paradigm to support mobile artificial intelligence (AI) applications at …

Network automation is a necessity in order to meet the unprecedented demand in the future
networks and zero touch network architecture is proposed to cater such requirements …

Federated learning (FL) has emerged as a promising alternative to centralized machine
learning for exploiting large amounts of data generated by networks while ensuring data …