Model-Centric Distributed Learning in Smart Community Sensing and Embedded Systems

Smart Community sensing is an efficient distributed paradigm that leverages the embedded sensors of community members to monitor the spatial-temporal phenomena in the environment, such as air pollution and temperature. The multi-party feature in community sensing increases the needs of distributed data collection, storage and processing, where it also benefits the privacy-preserved manner in different kinds of applications. Two types of distributed learning algorithms are usually used in community sensing, which is data-centric and model-centric. Each has its own merits on the variety of carriers for deployment. However, with the growth of embedded smart devices in real-world scenarios, we need to rethink and redesign the current distributed learning framework to appropriately deal with the trade-offs in these two classical models. In order to fully leverage the mobility, lite weight, low-cost and quick response of the embedded systems (devices), we propose multiple model-centric based distributed learning frameworks to handle the real-world cases/applications and demonstrate the superiority on the overall performance compared to the data-centric and centralized strategies. We will discuss the benefits of model-centric when embracing the community sensing and algorithm learning (training) on embedded systems.

CV:
Jiang Bian is a visiting Ph.D. student, supervised by Prof. Zhishan Guo at University of Central Florida (co-supervised by Prof. Haoyi Xiong, Baidu Research). His first two years of Ph.D are in Computer Science Department of Missouri University of Science and Technology. In advance of stepping in CECS doctoral research, he received my B.Eng degree of Logistics Systems Engineering in Huazhong University of Science and Technology in China, and earned my M.Sc degree of Industrial Systems Engineering in University of Florida. Jiang’s research interests include Human-subject Data Learning, Ubiquitous Computing and Intelligent Systems.