aiqbench analysis
This paper addresses the critical need for low-latency and resilient AI in civil aviation by shifting inference logic from centralized clouds to network edges. It reviews techniques like split learning and collaborative inference that enable offline operation while mitigating privacy risks associated with data centralization. The work establishes a framework for integrating edge intelligence into safety-critical flight operations, offering practical pathways for deploying robust models without compromising sovereignty or connectivity.
Edge Intelligence in Civil Aviation: Paradigms, Techniques, and Applications
arXiv:2607.19676v1 Announce Type: new Abstract: 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 panoramic view and a common understanding of edge intelligence tailored to civil aviation. We firstly articulate the operational motivations for edge AI, and then review recent techniques for edge inference and edge learning. We then introduce the organizational computing paradigms and the respective configurations in civil aviation environments; finally, we describe the emerging applications and the future research trends of edge intelligence in civil aviation. We argue that a refined edge solution can complement cloud foundations to deliver low latency, privacy preserving, and resilient AI services across the civil aviation lifecycle.
Abstract: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 panoramic view and a common understanding of edge intelligence tailored to civil aviation. We firstly articulate the operational motivations for edge AI, and then review recent techniques for edge inference and edge learning. We then introduce the organizational computing paradigms and the respective configurations in civil aviation environments; finally, we describe the emerging applications and the future research trends of edge intelligence in civil aviation. We argue that a refined edge solution can complement cloud foundations to deliver low latency, privacy preserving, and resilient AI services across the civil aviation lifecycle.
Artificial Intelligence (cs.AI)
arXiv:2607.19676 [cs.AI]
(or arXiv:2607.19676v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2607.19676 arXiv-issued DOI via DataCite (pending registration)
https://doi.org/10.1109/ICICSE66971.2025.11429888 DOI(s) linking to related resources
Full text under the publisher's open licence · 208 words
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