Keynote Speaker Ⅰ

Zhu Han

University of Houston, USA

IEEE fellow, AAAS fellow


Abstract: Ultra-massive multiple-input multiple-output (MIMO) is one of the key enablers in the forthcoming sixth generation (6G) networks to provide revolutionary mobile connectivity and high-speed data services by exploiting spatial diversity. Widely-utilized phased arrays relying on costly components make the implementation of ultra-massive MIMO in practice become prohibitive from both cost and power consumption perspectives. The recent developed reconfigurable holographic surfaces (RHSs) composing of densely packing sub-wavelength metamaterial elements can achieve holographic beamforming without costly hardware components. By leveraging the holographic principle, the RHS serves as an ultra-thin and lightweight surface antenna integrated with the transceiver, thereby providing a promising alternative to phased arrays for realizing ultra-massive MIMO. In this tutorial, we will first provide a basic introduction of RHSs. We then introduce the unique features of RHSs which enables both communication and sensing, in a comprehensive way. Related design, analysis, optimization, and signal processing techniques will be presented. Typical RHS-based applications for the wireless communications and radio-frequency sensing will be explored. The implementation issues along with our developed prototypes and experiments will also be discussed. Several up-to-date challenges and potential research directions will be discussed as well.

Bio: Zhu Han (Chinese character 韩竹) 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 in the Computer Science Department at the University of Houston, Texas. His research interests include wireless resource allocation and management, wireless communications and networking, game theory, big data analysis, security, and smart grid.  Dr. Han received an NSF Career Award in 2010, the Fred W. Ellersick Prize of the IEEE Communication Society in 2011, the EURASIP Best Paper Award for the Journal on Advances in Signal Processing in 2015, IEEE Leonard G. Abraham Prize in the field of Communications Systems (best paper award in IEEE JSAC) in 2016, and several best paper awards in IEEE conferences. Currently, Dr. Han is IEEE fellow since 2014, AAAS fellow since 2019 and ACM distinguished member since 2019. Dr. Han is 1% highly cited researchers according to Web of Science since 2017.

Keynote Speaker Ⅱ

Deshuang Huang

Eastern Institute of Technology, Ningbo, China

IEEE Fellow, IAPR Fellow, AAIA Fellow

Bio: De-Shuang Huang is a Professor in Institute of Machine Learning and Systems Biology, Eastern Institute of Technology, Ningbo, China. He is currently the foreign member of Russian Academy of Engineering, the Fellow of the IEEE (IEEE Fellow), the Fellow of the International Association of Pattern Recognition (IAPR Fellow), the Fellow of the Asia-Pacific Artificial Intelligence Association (AAIA), and associated editors of IEEE/ACM Transactions on Computational Biology & Bioinformatics and IEEE Transactions on Cognitive and Developmental Systems, etc. He founded the International Conference on Intelligent Computing (ICIC) in 2005. ICIC has since been successfully held annually with him serving as General or Steering Committee Chair. He also served as the 2015 International Joint Conference on Neural Networks (IJCNN2015) General Chair, July12-17, 2015, Killarney, Ireland, the 2014 11th IEEE Computational Intelligence in Bioinformatics and Computational Biology Conference (IEEE-CIBCBC) Program Committee Chair, May 21-24, 2014, Honolulu, USA. He has published over 480 papers in international journals, international conferences proceedings, and book chapters. Particularly, he has published over 260 SCI indexed papers. His Google Scholar citation number is 23822 times and H index 80. His main research interest includes neural networks, pattern recognition and bioinformatics.

Keynote Speaker Ⅲ

Xiaofeng Liao

Chongqing University, China

IEEE Fellow, AIAA Fellow

Invited Speaker 

Hai Cheng

Heilongjiang University

Title: Optimizing Federated Learning: A Novel DP-FedSAdam Algorithm for Enhanced Privacy and Performance

Abstract: Federated learning allows multiple parties to collaborate on training shared models in a privacy-preserving manner. But FL involves frequent communication between the server and the participant, which can compromise the privacy of the client. To address this, client-level differential privacy federated learning (DPFL) introduces locally updated clips and noise additions to improve privacy. However, this increased noise can degrade the performance of the model. In this study, we propose an optimization algorithm using SAM+ADAM optimizer, which aims to reduce the noise impact and the number of communication rounds to improve resource efficiency. The DP-FedSAdam proposed in this paper, combined with sharpness-perceptual minimization, creates a model with improved stability and resistance to weight perturbations, minimizing local update specifications and enhancing resilience to DP noise. As a result, it achieves optimal model parameters and reduced resource usage. A large number of experiments and comparative analyses verify the superiority of the proposed algorithm compared with traditional methods.

Bio: Hai Cheng, an associate professor at Heilongjiang University. His areas of interest include cryptography, network security, federated learning, computer graphics, and deep learning.

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