Federated Learning in V2X Networks

 

Federated Learning in V2X Networks

Tuesday, 07 February, 16:00

H. Vincent Poor from the University Princeton, is
giving a talk entitled

Federated Learning in V2X Networks

Abstract

The fifth generation and the emerging sixth generation of cellular networks
aim to support vehicular networks, including communication among vehicles,
pedestrians and road infrastructures, i.e., vehicle-to-everything (V2X) communications.
These networks face difficult wireless propagation conditions due to rapidly varying channels,
and must support low latency and high reliability, with vehicles forming dynamic topologies.
However, with the help of such networks, vehicular applications can apply distributed
machine learning techniques to enable assisted and self-driving systems.  
Federated learning (FL) is a collaborative distributed machine learning paradigm that is
well-suited to this application. This talk will introduce the fundamentals of FL over
wireless networks and discuss applications of FL in V2X communications, highlighting challenges,
solutions, and open problems arising from the integration of these two technologies.

Biography

H. Vincent Poor is the Michael Henry Strater University Professor at Princeton University,
where research interests are in the areas of information theory,
machine learning and network science, and their applications in wireless networks,
energy systems and related fields. He has also held visiting appointments at several
other universities as well, including most recently at Berkeley and Cambridge.
 Among his recent publications is the book Machine Learning and Wireless Communications 
(Cambridge University Press, 2022). Dr. Poor is a member of the
U.S. National Academy of Engineering and the U.S. National Academy of Sciences,
and he is a foreign member of the Royal Society and other national and international academies.
He received the IEEE Alexander Graham Bell Medal in 2017.