The next-generation of wireless networks will enable many machine learning (ML) tools and
applications to efficiently analyze various types of data collected by edge devices for …

The rapid advancement of artificial intelligence technologies has given rise to diversified
intelligent services, which place unprecedented demands on massive connectivity and …

Communication systems to date primarily aim at reliably communicating bit sequences.
Such an approach provides efficient engineering designs that are agnostic to the meanings …

Research on smart connected vehicles has recently targeted the integration of vehicle-to-
everything (V2X) networks with Machine Learning (ML) tools and distributed decision …

Federated learning (FL) has been gaining attention for its ability to share knowledge while
maintaining user data, protecting privacy, increasing learning efficiency, and reducing …

Classical and centralized Artificial Intelligence (AI) methods require moving data from
producers (sensors, machines) to energy hungry data centers, raising environmental …

Decentralized Federated Learning (DeFL) plays a critical role in improving effectiveness of
training and has been proved to give great scope to the development of edge computing …

Distributed optimization finds a wide range of applications ranging from machine learning to
vehicle platooning. To overcome the bottleneck caused by the required extensive message …

Multiple access (MA) is a crucial part of any wireless system and refers to techniques that
make use of the resource dimensions to serve multiple users/devices/machines/services …

Wireless federated learning (FL) is a collaborative machine learning (ML) framework in
which wireless client-devices independently train their ML models and send the locally …