Edge Intelligence in Civil Aviation: Paradigms, Techniques, and Applications
Civil aviation is safety critical and its operations, from flight decks and towers to ramps and maintenance, generate massive, heterogeneous data at the network edge. Yet cloud centric deployment of large Artificial Intelligence (AI) models often produces high task latency, lacks offline capability in communication denied environments, and requires centralizing sensitive data, raising privacy and sovereignty risks. Edge AI moves perception, prediction, and decision logic closer to the data producers via compression, collaborative inference, and split learning, thereby reducing latency, bandwidth, and exposure while enabling graceful operation during disconnections. This paper provides a p...
| Authors | Wenbin Li, Zhongtian Liao, Bolin Liu et al. |
|---|---|
| Primary category | cs.AI |
| Published | 22 July 2026 |
| Last updated at source | 22 July 2026 |
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