Prof. Zhu Han
IEEE Fellow, AAAS Fellow, ACM Fellow
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.
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