22/09/2026
๐ฌ ๐๐๐ฆ๐ข๐ง๐๐ซ ๐จ๐ง ๐๐จ๐ฅ๐ฅ๐๐๐จ๐ซ๐๐ญ๐ข๐ฏ๐ ๐๐ง๐ญ๐๐ฅ๐ฅ๐ข๐ ๐๐ง๐๐ ๐จ๐ง ๐๐๐ซ๐ฌ ๐โก๏ธ
Distributed AI/ML is a key application domain of modern edge computing. By bringing computational resources, particularly GPUs, closer to end users, it becomes possible to execute deep learning and large language models within a single wireless hop. In this talk, we introduce the concept of virtual edge computing, which bridges the gap between cloud, edge, and fog infrastructures by virtualising all available resources, including end-user devices. From the perspective of vehicular networking applications, this paradigm opens up new directions in the emerging field of collaborative intelligence. Rather than executing AI models exclusively on local devices or in the cloud, Edge AI enables a continuum where inference and learning tasks are distributed and collaboratively executed. This approach supports a wide range of novel applications developed by, for, and with connected vehicles. At the same time, disruptions and large-scale outages of communication infrastructure represent a realistic threat to mission-critical systems and can significantly impact daily life. Insights gained from past natural disasters have highlighted both key vulnerabilities and potential solution spaces. Accordingly, we also discuss strategies to enhance the resilience of collaborative intelligence systems.
๐
๐๐ฎ๐๐ฒ: September 24, 2026 (Thursday)
โฐ ๐ง๐ถ๐บ๐ฒ: 3:30 pm โ 4:30 pm
๐ ๐ฉ๐ฒ๐ป๐๐ฒ: Tam Wing Fan Innovation Wing Two, G/F, Run Run Shaw Building, The University of Hong Kong
๐ ๐๐ฒ๐๐ฎ๐ถ๐น๐: https://ece.hku.hk/events/20260924-1
๐ญ๐๐๐๐๐ ๐ฐ๐ฎ/๐ญ๐ฉ/๐น๐๐
๐๐๐๐: ๐ฏ๐ฒ๐ผ๐ฌ๐ช๐ฌ