Keynote Speakers
Prof. Jiangzhou Wang
International Member of the Chinese Academy of Engineering (CAE)
Fellow of the Royal Academy of Engineering (RAEng), U.K.
Fellow of IEEE, Fellow of IET
University of Kent, UK
Biography: Jiangzhou Wang is a Professor with the University of Kent, U.K. He has published more than 500 papers and five books. His research interest is in mobile communications. He was a recipient of the 2022 IEEE Communications Society Leonard G. Abraham Prize. He was the Technical Program Chair of the 2019 IEEE International Conference on Communications (ICC2019), Shanghai, Executive Chair of the IEEE ICC2015, London, and Technical Program Chair of the IEEE WCNC2013. He is/was the editor of multiple international journals, including IEEE Transactions on Communications from 1998 to 2013. Professor Wang is an International Member of the Chinese Academy of Engineering (CAE), a Fellow of the Royal Academy of Engineering (RAEng), U.K., Fellow of the IEEE, and Fellow of the IET.
Prof. Zhu Han
Fellow of IEEE, Fellow of AAAS, Fellow of ACM
University of Houston, USA
Speech Title: Federated Learning and Analysis
with Multi-access Edge Computing
Abstract: In recent years, mobile devices are equipped
with increasingly advanced computing capabilities, which opens up
countless possibilities for meaningful applications. Traditional
cloud-based Machine Learning (ML) approaches require the data to be
centralized in a cloud server or data center. However, this results in
critical issues related to unacceptable latency and communication
inefficiency. To this end, multi-access edge computing (MEC) has been
proposed to bring intelligence closer to the edge, where data is
originally generated. However, conventional edge ML technologies still
require personal data to be shared with edge servers. Recently, in light
of increasing privacy concerns, the concept of Federated Learning (FL) has
been introduced. In FL, end devices use their local data to train a local
ML model required by the server. The end devices then send the local model
updates instead of raw data to the server for aggregation. FL can serve as
enabling technology in MEC since it enables the collaborative training of
an ML model and also enables ML for mobile edge network optimization.
However, in a large-scale and complex mobile edge network, FL still faces
implementation challenges with regard to communication costs and resource
allocation. In this talk, we begin with an introduction to the background
and fundamentals of FL. Then, we discuss several potential challenges for
FL implementation such as unsupervised FL and matching game based
multi-task FL. In addition, we study the extension to Federated Analysis
(FA) with potential applications such as federated skewness analytics and
federated anomaly detection.
Biography: Zhu Han received the B.S. degree in electronic
engineering from Tsinghua University, in 1997, and the M.S. and Ph.D.
degrees in electrical and computer engineering from the University of
Maryland, College Park, in 1999 and 2003, respectively. From 2000 to 2002,
he was an R&D Engineer of JDSU, Germantown, Maryland. From 2003 to 2006,
he was a Research Associate at the University of Maryland. From 2006 to
2008, he was an assistant professor at Boise State University, Idaho.
Currently, he is a John and Rebecca Moores Professor in the Electrical and
Computer Engineering Department as well as the Computer Science Department
at the University of Houston, Texas. Dr. Han is an NSF CAREER award
recipient of 2010, and the winner of the 2021 IEEE Kiyo Tomiyasu Award. He
has been an IEEE fellow since 2014, an AAAS fellow since 2020,
an ACM fellow since 2024, an IEEE
Distinguished Lecturer from 2015 to 2018, and an ACM Distinguished Speaker
from 2022-2025. Dr. Han is also a 1% highly cited researcher since 2017.
Prof. Wei Xiang
Fellow of IET, Fellow of Engineers Australia
Vice Chair of the IEEE Northern Australia Section
La Trobe University, Australia
Biography: Wei Xiang (S’00–M’04–SM’10) Professor Wei Xiang is Cisco Research Chair of AI and IoT, Director of the Cisco-La Trobe Centre for AI and IoT at La Trobe University, and Founding Director of the Australian Centre for AI in Medical Innovation. Previously, he was Foundation Chair and Head of Discipline of IoT Engineering at James Cook University, Cairns, Australia. Due to his instrumental leadership in establishing Australia’s first accredited Internet of Things Engineering degree program, he was inducted into Pearcy Foundation’s Hall of Fame in October 2018. He is an elected Fellow of the IET in UK and Engineers Australia. He received the TNQ Innovation Award in 2016, and Pearcey Entrepreneurship Award in 2017, and Engineers Australia Cairns Engineer of the Year in 2017. He was a co-recipient of four Best Paper Awards at WiSATS’2019, WCSP’2015, IEEE WCNC’2011, and ICWMC’2009. He has been awarded several prestigious fellowship titles. He was named a Queensland International Fellow (2010-2011) by the Queensland Government of Australia, an Endeavour Research Fellow (2012-2013) by the Commonwealth Government of Australia, a Smart Futures Fellow (2012-2015) by the Queensland Government of Australia, and a JSPS Invitational Fellow jointly by the Australian Academy of Science and Japanese Society for Promotion of Science (2014-2015). He was the Vice Chair of the IEEE Northern Australia Section from 2016-2020. He was an Editor for IEEE Communications Letters (2015-2017), and is currently an Associate Editor for IEEE Communications Surveys & Tutorials, IEEE Internet of Things Journal, IEEE Access, and Nature journal of Scientific Reports. He has published over 250 peer-reviewed papers including 3 books and 200 journal articles. He has severed in a large number of international conferences in the capacity of General Co-Chair, TPC Co-Chair, Symposium Chair, etc. His research interest includes the Internet of Things, wireless communications, machine learning for IoT data analytics, and computer vision.