By Professor Anthony Kuh, University of Hawaii, USA
In recent years there have been many applications where data comes from distributed sources and with the increasing computational capabilities of edge devices more computing, processing and machine learning is done at the edge. Applications range from monitoring the environment to healthcare to transportation to energy to social networking. The edge devices could be sensors from a sensor network, IoT devices, mobile phones, or intelligent assistants. The edge devices learn from data they receive with popular methods including Federated Learning (FL). FL has advantages with learning done by edge devices thereby eliminating the need for data to be transmitted to the cloud or central processor. These advantages include communication savings, increased security, and addressing privacy concerns. In this presentation, we discuss real-time FL using kernel methods. We discuss real-time learning as much of the data in applications is dynamic or streaming requiring online learning and decision-making. Kernel methods are used as online nonlinear linear algorithms can easily be developed using tools from adaptive signal processing and convex optimization approaches. We show how online kernel methods can be modified for distributed learning and FL. Online FL kernel algorithms are developed, analyzed, and compared via simulation studies.
Anthony Kuh received his B.S. in Electrical Engineering and Computer Science at the University of California, Berkeley in 1979, an M.S. in Electrical Engineering from Stanford University in 1980, and a Ph.D. in Electrical Engineering from Princeton University in 1987. Dr. Kuh previously worked at AT&T Bell Laboratories and has been on the faculty in Electrical Engineering at the University of Hawai’i since 1986. He is currently a Professor in the Department, serving as director of the interdisciplinary renewable energy and island sustainability (REIS) group. Previously, he served as Department Chair of Electrical Engineering. Dr. Kuh’s research is in the area of neural networks and machine learning, adaptive signal processing, sensor networks, and renewable energy and smart grid applications. Dr. Kuh won a National Science Foundation Presidential Young Investigator Award and is an IEEE Fellow. He was also a recipient of the Boeing A. D. Welliver Fellowship and received a Distinguished Fulbright Scholar’s Award working at Imperial College in London. From 2017 to 2021, he served as program director for NSF in the Electrical, Communications, and Cyber Systems (ECCS) division working in the Energy, Power, Control, and Network (EPCN) group. At NSF he also assisted in initiatives including Harnessing the Data Revolution (HDR), the Mathematics of Deep Learning (MoDL), the AI Institutes, Cyber Physical Systems (CPS), and Smart and Connected Communities. He currently serves on the Awards Board of the IEEE Signal Processing Society and is President of the Asia Pacific Signal and Information Processing Association.