Demo: Split Computing-Based Privacy-Preserving Image Classification and Object Detection

Split computing (SC) introduces an innovative framework designed for inference operations of deep neural networks (DNNs), enhancing traditional machine learning inference approaches that typically deploy models solely on local devices or edge/cloud servers. This research not only implements the SC framework for distributed machine learning inference but also showcases its application in computer vision tasks. We demonstrate the capability of SC to execute privacy-preserving distributed inferences across both image classification and object detection tasks. Our comprehensive implementation will be made publicly available at https://github.com/nishio-laboratory/.