Edge–Cloud Intelligence for Scalable AI Applications: A Review of Model Deployment, Resource Orchestration, Privacy, and Energy Efficiency
Keywords:
AI inference, Edge computing, Federated learning, Mobile cloud computing, Resource orchestration, Sustainable AIAbstract
AI workloads increasingly span devices, gateways, edge servers, and centralized clouds, coupling model placement with transfer time, compute energy, prediction quality, cost, and security boundaries. This structured critical review synthesizes 54 selected studies across deployment placement, model compression, orchestration, federated privacy, industrial monitoring, and environmental accounting. It provides explicit inclusion criteria and replicable database search strings, as well as an audited, non-exclusive thematic map of the cited bibliography; an exhaustive database search and PRISMA-ScR screening flow were not performed. Source-specific evidence includes handheld inference latencies, reported compression and energy gains, a mobile–cloud layer-partitioning study, and an industrial compressor deployment. To connect these results, the study formulated a constrained route- and model-selection problem accounting for shared model memory, split inference, tail-latency deadlines, energy, and trust boundaries. A formal (epsilon, delta)-differential privacy definition and threat-model matrix distinguish verifiable safeguards from locality-based assumptions. The study concludes that end-to-end evaluation under matched workloads is necessary before claiming device-, edge-, or cloud-tier superiority. The review provides an analytical evaluation framework, not a newly validated system or complete systematic evidence census.
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