Cloud–Edge Collaborative Learning and Model Compression Framework for Communication-Assisted Human Action Recognition

The growing demand for efficient human action recognition has led to significant advancements in applications such as surveillance, healthcare, and human–computer interaction. This paper uses the Widar3.0 dataset to evaluate the proposed a cloud–edge collaborative learning framework that incorporates model compression techniques, including knowledge distillation, pruning, and quantization, to optimize computational resource usage and improve recognition accuracy. Focusing on human action recognition, the framework was structured across two layers: the cloud and the edge devices, each handling specific tasks such as global model updates, intermediate model aggregation, and lightweight inference, respectively. Experimental results showed that the proposed framework achieved an accuracy of 89.7%, outperforming traditional models by 4.4%. Moreover, the communication overhead per round was reduced by 43%, decreasing from 100 MB to 57 MB. The framework demonstrated improved performance as the number of edge devices increased, with the accuracy rising to 89.0% with 10 devices. These results validate the effectiveness of the proposed system in achieving high recognition accuracy while significantly reducing resource consumption for human action recognition.