CityU SDSC Seminar – Data-driven Specialization towards Intelligent Internet of Things

 The CityU Online Seminar named “Data-driven Specialization towards Intelligent Internet of Things” was organized by The School of Data Science (SDSC) on 26^th April 2022.  This talk invited Dr. Zimu Zhou (Assistant Professor, School of Computing and Information Systems, Singapore Management University) as guest speaker and he would like to share data-riven approach that optimizes the computation to the application context in order to break the resource limits of IoT devices and systems. Two examples included compression of multiple deep neural networks for mobile computer vision and post-deployment sensor calibration for air pollution monitoring were shared.

In the beginning, Dr. Zimu Zhou briefed the background of Internet of Things (IoT) since 1999.  After 20+ years development, massive data generated from city and intelligent applications were popular that IoT as embedded system but we faced grand challenge on high performance against low resource.

Firstly, Dr. Zhou discussed the question “how to customize advanced data processing such as deep learning to IoT context for efficient deployment?” He was inspired from biology about network pruning.

An overview of network pruning taxonomy was discussed including what to prune? how to prune? When to prune? and how often? It was assumed to remove redundant parameters within single DNN. Some IoT applications were discussed.

Multitask Zipping (MTZ) and its merge pre-trained DNN via neuron sharing were introduced. He also briefed some problems of pruning a multitask DNN including “if there are task B exclusive parameters in the shared part, pruning may eliminate parameters crucial for task B” that creates conflicts and “if there are shared parameters duplicated in the task exclusive parts, pruning may not remove such redundancy” that it was unable to merge duplicated neurons into shared parts.

After that Dr. Zhou discussed the 2^nd question “how to enhance IoT applications governed by domain sciences with big data?” He then briefed some sensor employed in air pollution monitoring but those low cost & low power sensors had the data quality problem. He proposed calibration transfer for automatic calibration of sensors without access to references. And future work he proposed was on-device calibration.

Finally, Dr. Zhou purposed collaborative intelligent IoT and employed cross-device federated learning. Deploying federated learning to IoT applications needed on-device model training.

Lastly, Dr. Zhou mentioned intelligent transport deploying federated learning to IoT applications and thanks his collaborators.

Reference:

SDSC - https://www.sdsc.cityu.edu.hk/

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