Keynote Speakers

Prof. Yu Wang (IEEE Fellow) 

Temple University, USA

Special Title: Optimization of Federated Learning at Edge Clouds: Participant Selection and Learning Scheduling
Abstract: Edge computing and federated learning (FL) have gained popularity since they provide a promising edge learning framework that mitigates the limitations of long latency, high cost, and privacy concerns in cloud-based centralized learning. While most existing works on federated learning over edge systems focus on optimizing the training of the global model in edge systems, the concurrent training of multiple FL models from different applications in a shared edge cloud can lead to edge resource competition and affect the training performance of each model. Hence, in this talk, I will present our recent works in addressing this challenge by proposing optimization algorithms to jointly select FL participants and learning rates or topologies for each model, with an aim of minimizing the total training cost. I will first introduce a multi-stage optimization framework that allows FL models to select their participants and learning rates or learning topologies. Then, I will describe a quantum assisted algorithm to tackle the joint participant selection and learning scheduling problem using both quantum and classical computing. Last, I will briefly discuss our proposed solution for group formation and group sampling for group-based FL over edge systems.

Biodata: Yu Wang is a Professor in the Department of Computer and Information Sciences at Temple University. He holds a Ph.D. from Illinois Institute of Technology, an MEng and a BEng from Tsinghua University, all in Computer Science. His research interest includes wireless networks, smart sensing, and mobile computing. He is a recipient of Ralph E. Powe Junior Faculty Enhancement Awards from Oak Ridge Associated Universities (2006), Outstanding Faculty Research Award from University of North Carolina at Charlotte (2008), Fellow of IEEE (2018), and ACM Distinguished Member (2020). He has served as Associate Editor for IEEE Transactions on Parallel and Distributed Systems, IEEE Transactions on Cloud Computing, among others.


Prof. Makoto Iwasaki (IEEE Fellow) 

Nagoya Institute of Technology, Japan

Speech Title: GA-Based Practical System Identification and Auto-Tuning for Multi-Axis Industrial Robots
Abstract: Fast-response and high-precision motion control is one of indispensable techniques in a wide variety of high performance mechatronic systems including micro and/or nano scale motion, such as data storage devices, machine tools, manufacturing tools for electronics components, and industrial robots, from the standpoints of high productivity, high quality of products, and total cost reduction. In those applications, the required specifications in the motion performance, e.g. response/settling time, trajectory/settling accuracy, etc., should be sufficiently achieved. In addition, the robustness against disturbances and/or uncertainties, the mechanical vibration suppression, and the adaptation capability against variations in mechanisms should be essential properties to be provided in the performance.
The keynote speech presents a practical auto-tuning technique based on a genetic algorithm (GA) for servo controllers of multi-axis industrial robots. Compared to conventional manual tuning techniques, the auto-tuning technique can save the time and cost of controller tuning by skilled engineers, reduce performance deviation among products, and achieve higher control performance. The technique consists of two main processes: one is an autonomous system identification process, involving the use of actual motion profiles of a typical robot. The other is an autonomous control gain tuning process in the frequency and time domains, involving the use of GA, which satisfies the required tuning control specifications, e.g., control performance, execution time, stability, and practical applicability in industries. The proposed technique has been practically evaluated through experiments performed with an actual six-axis industrial robot.

Biodata: Makoto Iwasaki received the B.S., M.S., and Dr. Eng. degrees in electrical and computer engineering from Nagoya Institute of Technology, Nagoya, Japan, in 1986, 1988, and 1991, respectively. He is currently a Professor at the Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology.
As professional contributions of the IEEE, he has participated in various organizing services, such as, a Co-Editors-in-Chief for IEEE Transactions on Industrial Electronics from 2016 to 2022, a Vice President for Planning and Development in term of 2018 to 2021, etc. He is IEEE fellow class 2015 for "contributions to fast and precise positioning in motion controller design".
He has received many academic, foundation, and government awards, like the Best Paper and Technical Awards of IEE Japan, the Nagamori Award, the Ichimura Prize, and the Commendation for Science and Technology by the Japanese Minister of Education, respectively. He is also a fellow of IEE Japan, and a member of Science Council of Japan.
His current research interests are the applications of control theories to linear/nonlinear modeling and precision positioning, through various collaborative research activities with industries.

 

Speakers in 2024 to be announced soon......