Privacy-preserving splitting of neural network models for prediction across multiple devices

Abstract

A first electronic device and a method for implementation for privacy-preserving splitting of neural network (NN) models for prediction across multiple devices is disclosed. The first electronic device applies an NN model on a received input datapoint to determine a set of feature maps. Each feature map is compared with the received input datapoint to determine a first NN sub-model including a first sub-set of NN layers. The determined first NN sub-model is applied on the received input datapoint to determine a first feature map. The first electronic device transmits the determined first feature map and information indicative of a second sub-set of NN layers of a second NN sub-model to a second electronic device. The second NN sub-model is used to determine a prediction output. The first electronic device receives the determined prediction output and controls rendering of the received prediction output on a display device.