Towards Practical Domain Adaptation and Generalization in IoT Sensing

MPhil Thesis Defence


Title: "Towards Practical Domain Adaptation and Generalization in IoT Sensing"

By

Mr. Zhiyang LI


Abstract

With the prevalence of Internet-of-Things (IoT) devices, IoT sensing 
technologies are also quickly evolving and gradually transforming our 
lifestyles. Deep Learning techniques, which have shown great success in many 
areas, are also being adopted in IoT sensing applications. However, we may not 
have enough data for Deep Learning model training, or only have unlabeled 
sensor data, which is very common in IoT sensing scenarios. As an important 
branch in Transfer Learning, Domain Adaptation and Generalization techniques 
can be applied in such scenarios to train the target model with the help of 
related source datasets. However, currently there is not much work that 
explores the application of Domain Adaptation and Generalization in IoT sensing 
scenarios. Their effect on real IoT sensing problems is not fully studied. 
Moreover, current works do not consider the model training problem in real IoT 
environment. With the increased attention of the public on data privacy, it 
becomes more and more difficult to collect all data in a data center for 
centralized training. Therefore, a distributed model training framework is 
needed for practical Domain Adaptation and Generalization in IoT environment.

In this thesis, we first describe our exploration to apply Domain 
Generalization to a practical IoT sensing problem, which is driver monitoring 
using FMCW radar. We tested 2 different Domain Generalization methods, namely 
CCSA and MMD-AAE, and their modifications, in an attempt to reduce the amount 
of real driving data needed and make the model perform well on unseen drivers 
and cars. Then we will introduce our design of a Federated Domain Adaptation 
framework, which is a distributed training framework tailored for Domain 
Adaptation training in IoT environment. It incorporates Federated Learning and 
Homomorphic Encryption to protect data privacy of all participants. Moreover, 
we design 2 optimization methods which further reduce the computation and 
communication overhead during training, making our framework more friendly to 
resource-constrained IoT devices.


Date:  			Friday, 7 August 2020

Time:			2:00pm - 4:00pm

Zoom meeting:		https://hkust.zoom.us/j/96372552867

Committee Members:	Prof. Qian Zhang (Supervisor)
  			Prof. Gary Chan (Chairperson)
 			Dr. Kai Chen


**** ALL are Welcome ****