Keynote Speakers

 

Prof. Zhu Han

AAAS Fellow, IEEE fellow, John and Rebecca Moores Professor

University of Houston, USA

Speech Title: Federated Learning and Analysis with Multi-access Edge Computing
 

Abstract:: With the maturity of edge computing and the large amount of data generated by IoT devices, we have witnessed an increasing number of intelligent applications in wireless networks. The growing awareness of privacy further motivates the wide study and deployment of federated learning, a collaborative distributed model training framework for predictive tasks. However, a wide range of applications, more broadly relevant to data analytics and query in wireless networks, cannot be well supported by this framework. These applications usually require more complex and diverse aggregation methods, instead of the simple weight aggregations, and are broadly nourished by statistics, information theory, and signal processing, besides machine learning. This talk aims to present the recent advances in federated analytics at the intersection of data science, wireless communication, and security and privacy. We will present the definition, taxonomy, and architecture of the federated analytics techniques. It will also cover several practical and important data analytics tasks in wireless networks, including federated anomaly detection, federated frequent pattern analysis, federated distribution estimation and skewness analytics. Finally, we will present important challenges, open problems, and future directions at the intersection of federated learning/analysis and wireless networks.

Short bio:  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 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 in Boise State University, Idaho. Currently, he is a John and Rebecca Moores Professor in Electrical and Computer Engineering Department as well as Computer Science Department at University of Houston, Texas. His research interests include security, wireless resource allocation and management, wireless communication and networking, game theory, and wireless multimedia. Dr. Han is an NSF CAREER award recipient 2010. Dr. Han has several IEEE conference best paper awards, and winner of 2011 IEEE Fred W. Ellersick Prize, 2015 EURASIP Best Paper Award for the Journal on Advances in Signal Processing and 2016 IEEE Leonard G. Abraham Prize in the field of Communication Systems (Best Paper Award for IEEE Journal on Selected Areas on Communications). Dr. Han has been IEEE fellow since 2014, AAAS fellow since 2020 and IEEE Distinguished Lecturer from 2015 to 2018. Dr. Han is winner of 2021 IEEE Kiyo Tomiyasu Award, and has been 1% highly cited researcher according to Web of Science since 2017.

 

 

Prof. PUN Chi Man

Department of Computer and Information Science
Faculty of Science and Technology, University of Macau
Avenida da Universidade, Taipa, Macau, China

Speech Title: Image Splicing Localization with Deep Neural Networks
 

Abstract: Creating fake pictures has become more accessible than ever, but tampered images are more harmful because the Internet propagates misleading information so rapidly. Reliable digital forensic tools are, therefore, strongly needed. Traditional methods based on hand-crafted features are only useful when tampered images meet specific requirements, and the low detection accuracy prevents them from being used in realistic scenes. Recently proposed learning-based methods improve the accuracy, but neural networks usually require to be trained on large labeled databases. This is because commonly used deep and narrow neural networks extract high-level visual features and neglect low-level features where there are abundant forensic cues. In this talk, we will discuss some solutions to this problem. Two novel image splicing localization methods are proposed using deep neural networks, which mainly concentrate on learning low-level forensic features and consequently can detect splicing forgery, although the network is trained on a small automatically generated splicing dataset.

Short bio: Prof. Pun received his Ph.D. degree in Computer Science and Engineering from the Chinese University of Hong Kong in 2002, and his M.Sc. and B.Sc. degrees from the University of Macau. He had served as the Head of the Department of Computer and Information Science, University of Macau from 2014 to 2019, where he is currently a Professor and in charge of the Image Processing and Pattern Recognition Laboratory. He has investigated many externally funded research Projects as PI, and has authored/co-authored more than 200 refereed papers in many top-tier Journals (including T-PAMI, T-IFS, T-IP, T-DSC, T-KDE, and T-MM) and Conferences (including CVPR, ICCV, ECCV, AAAI, ICDE, IJCAI, MM, and VR). He has also co-invented several China/US Patents, and is the recipient of the Macao Science and Technology Award 2014 and the Best Paper Award in the 6th Chinese Conference on Pattern Recognition and Computer Vision (PRCV2023). Dr. Pun has served as the General Chair for the 10th &11th International Conference Computer Graphics, Imaging and Visualization (CGIV2013, CGIV2014), the 13th IEEE International Conference on e-Business Engineering (ICEBE2016), and the General Co-Chair for the IEEE International Conference on Visual Communications and Image Processing (VCIP2020) and the International Workshop on Advanced Image Technology (IWAIT2022), and the Program/Local Chair for several other international conferences. He has also served as the SPC/PC member for many top CS conferences such as AAAI, CVPR, ICCV, ECCV, MM, etc. He has been listed in the World's Top 2% Scientists by Stanford University since 2020. His research interests include Image Processing and Pattern Recognition; Multimedia Information Security, Forensic and Privacy; Adversarial Machine Learning and AI Security, etc. He is also a senior member of the IEEE.