Resource-Aware DNN Partitioning for Privacy-Sensitive Edge-Cloud Systems

With recent advances in deep neural networks (DNNs), there is a significant increase in IoT applications leveraging AI with edge-cloud infrastructures. Nevertheless, deploying large DNN models on resource-constrained edge devices is still challenging due to limitations in computation, power, and application-specific privacy requirements. Existing model partitioning methods, which deploy a partial DNN on an edge device while processing the remaining portion of the DNN on the cloud, mainly emphasize communication and power efficiency. However, DNN partitioning based on the privacy requirements and resource budgets of edge devices has not been sufficiently explored in the literature. In this paper, we propose awareSL, a model partitioning framework that splits DNN models based on the computational resources available on edge devices, preserving the privacy of input samples while maintaining high accuracy. In our evaluation of multiple DNN architectures, awareSL effectively identifies the split points that adapt to resource budgets of edge devices. Meanwhile, we demonstrate the privacy-preserving capability of awareSL against existing input reconstruction attacks without sacrificing inference accuracy in image classification tasks.