Zoom Link
http://hku.zoom.us/j/7074144117?omn=95813783034
Abstract
Cooperative edge AI enables edge devices and edge servers to collaboratively execute intelligent tasks under limited computation, storage, energy, and communication resources. In this talk, we discuss two complementary research directions toward communication-efficient cooperative edge AI. First, we introduce an event-triggered cooperative inference framework for rare-event detection in edge intelligence systems. Rare events are usually infrequent but highly critical, while conventional edge inference systems may overlook them due to data imbalance and rigid resource allocation. To address this issue, a dual-threshold multi-exit architecture is adopted, allowing confident normal events to be processed locally while complex or uncertain rare events are selectively offloaded to the edge server for more accurate classification. Second, we present an efficient AI model downloading framework based on parametric-sensitivity-aware retransmission. Instead of treating all model parameters equally, this framework exploits the unequal importance of neural network parameters and allocates wireless retransmission resources to more sensitive model packets. In this way, downloading latency can be reduced while inference performance is preserved. The talk concludes with a discussion of future research directions in cooperative edge AI, highlighting open challenges and opportunities in communication-efficient inference, adaptive model deployment, and resource-aware edge intelligence.
Speaker
Mr Zhou You
Department of Electrical and Computer Engineering
The University of Hong Kong
Biography of the Speaker
Zhou You is currently pursuing a Ph.D. degree in the Department of Electrical and Electronic Engineering at The University of Hong Kong, under the supervision of Prof. Kaibin Huang. He received his B.Eng. degree in Electrical Engineering from the University of Wisconsin–Madison, USA, in 2021. His research interests include wireless communications, edge inference, and AI model downloading.
Organiser
Prof. Kaibin HUANG
Department of Electrical and Computer Engineering, The University of Hong Kong
All are welcome.