Due to the edge's position between the cloud and the users, and the recent surge of deep neural network (DNN) applications, edge computing brings about uncertainties that must be understood separately. Particularly, the edge users' locally specific requirements that change depending on time and location cause a phenomenon called dataset shift, defined as the difference between the training and test datasets' representations. It renders many of the state-of-the-art approaches for resolving uncertainty insufficient. Instead of finding ways around it, we exploit such phenomenon by utilizing a new principle: AI model diversity, which is achieved when the user is allowed to opportunistically choose from multiple AI models. To utilize AI model diversity, we propose Model Diversity Network (MoDNet), and provide design guidelines and future directions for efficient learning driven communication schemes.
|Title of host publication||2022 IEEE 95th Vehicular Technology Conference - Spring, VTC 2022-Spring - Proceedings|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Publication status||Published - 2022|
|Event||95th IEEE Vehicular Technology Conference - Spring, VTC 2022-Spring - Helsinki, Finland|
Duration: 2022 Jun 19 → 2022 Jun 22
|Name||IEEE Vehicular Technology Conference|
|Conference||95th IEEE Vehicular Technology Conference - Spring, VTC 2022-Spring|
|Period||22/6/19 → 22/6/22|
Bibliographical noteFunding Information:
This work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by
© 2022 IEEE.
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Electrical and Electronic Engineering
- Applied Mathematics