<![CDATA[The proliferation of Internet of Things (IoT) devices and the demand for real-time processing have positioned edge computing as critical infrastructure for deploying deep learning models near the data source. Hardware constraints, including limited memory, narrow computational budgets, and strict power limits, challenge deployment of large-parameter neural architectures. This review examines hybrid architectures integrating convolutional neural networks (CNN) with attention mechanisms for local feature extraction on resource-constrained edge devices. Following the PRISMA 2020 protocol, a multi-stage search was conducted exclusively on Scopus, yielding 3,571 records, screened until 121 high-quality studies were included. The review addresses five research questions covering architectural trends, efficiency strategies, performance trade-offs, application domains, and federated learning for privacy-preserving deployment. Findings show that hybrid CNN-Attention architectures outperform pure CNN and Transformer baselines, with 3.2% average accuracy improvement while maintaining competitive inference latency on platforms such as NVIDIA Jetson and Raspberry Pi. Depthwise separable convolution, efficient channel attention, and token aggregation emerged as dominant compression strategies, with health imaging and fault diagnosis as leading domains. The review concludes that hybrid architectures represent the state of the art for edge-oriented local feature extraction, with future directions toward heterogeneous node generalization and multi-modal sensor fusion for distributed IoT networks.]]>
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