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<td><img alt="Dissertation Defense Announcement at the Cullen College of Engineering" width="600" height="171" src="https://www.egr.uh.edu/sites/www.egr.uh.edu/files/enews/2022/images/dissertation1.png">
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<div style="font-size:24px; color:rgb(200,16,46); line-height:28px"><b>Towards Intelligent Mobile Edging: High Efficiency, Low Cost, and Good Decision</b></div>
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<div style="font-size:18px; margin-bottom:5px"><strong>Dian Shi</strong></div>
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April 15, 2022; 8:00 AM - 10:00 AM (CST)<br>
Location: Zoom Meeting<br>
Zoom: <span style="font-family:Roboto,RobotoDraft,Helvetica,Arial,sans-serif"><a href="https://urldefense.com/v3/__https://uh-edu-cougarnet.zoom.us/j/9167221115__;!!LkSTlj0I!FDY9GW8FLA7TtEU0VGUanALOCKo8_4APrwyN9-ID9GiYcqMOA1p_vqT4oSQNameHXAkKTURUM02NpGihbutmVA$" data-auth="NotApplicable">https://uh-edu-cougarnet.zoom.us/j/9167221115</a></span></p>
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<strong>Committee Chair:</strong><br>
Miao Pan, Ph.D.<br>
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<strong>Committee Members:</strong><br>
Zhu Han, Ph.D. | David Mayerich, Ph.D. | Xin Fu, Ph.D. | Tomoaki Ohtsuki, Ph.D.</p>
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<strong>Abstract</strong></p>
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In recent years, with the continuous prosperity of the mobile Internet industry and the Internet of Things, the advance of widely used mobile terminals promotes the integration of mobile networks and Artificial Intelligence (AI), two of the most disruptive
technologies world has seen nowadays. While each technology has spawned a large number of applications to facilitate our lives, the combination of mobile networks and AI, i.e., intelligent mobile edging, is going to be genuinely transformative. One perspective
of combining these two is to exploit mobile networks to better support AI (Wireless for AI), where federated learning (FL) over mobile devices can greatly extend the scale of AI functions and preserve data privacy. Another perspective of the combination of
AI and mobile networks is to tackle the challenges of wireless communication with AI strengths (AI for Wireless). Specifically, AI techniques can represent hard-to-model wireless problems and find feasible solutions with low computational complexity. The last
point is that the combination can enable various smart mobile applications and services (AI & Wireless for applications). Though intelligent mobile edging has infiltrated many areas due to its advantages, several critical challenges still limit the efficient
implementation of intelligent mobile edging, among which efficiency, cost, and performance are major considerations of this dissertation.<br>
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Huge energy/time consumption is one of the most significant obstacles restricting the development of AI functions on resource-constrained mobile devices. Besides, the delay-sensitive property of intelligent mobile applications also puts forward higher requirements
for the efficiency of AI methodologies and communication service decision-making. Therefore, given these challenges, the objectives of this dissertation are to develop high efficiency, low cost, and good decision intelligent mobile edging methodologies from
the three perspectives mentioned above through a combination of theoretical, simulation, and experimental studies. Specifically, this dissertation firstly endeavors to develop a series of efficient FL over mobile devices approaches, where computing and communication
resources are well balanced to reduce the total cost during training; and then focuses on making good decisions and improving the performance of implementing AI functions on wireless networks and intelligent wireless services.<br>
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<td><img alt="Engineered For What's Next" width="600" height="82" src="https://www.egr.uh.edu/sites/www.egr.uh.edu/files/enews/2022/images/dissertation2.png"></td>
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