Robust Mobile-Cloud Collaborative CNN Inference under Unreliable Wireless Networks

Mobile-cloud collaborative Convolutional Neural Network (CNN) inference enables the efficient execution of CNN models by offloading partial inference workloads from mobile devices to the cloud. Although model partitioning for collaborative inference has been extensively studied, most existing approaches assume reliable mobile-cloud transmission, which often breaks down in real-world wireless environments with packet loss. In such scenarios, incomplete feature transmission can result in a significant drop in inference accuracy. In this paper, a joint scheduling approach is proposed to address this challenge, leveraging a search-based algorithm to determine both the model partition layer and redundancy level, to balance inference accuracy and latency under packet loss conditions. The proposed method is evaluated in a real-world mobile-cloud environment. Results show that it reduces the inference latency by up to 30.3% compared to the non-redundant baseline with the same accuracy threshold.