A Coalition Formation Approach for Privacy and Energy-Aware Split Deep Learning Inference in Edge Camera Network

Recently, an increasing amount of application tasks have depended on deep learning (DL) inference models on Internet of Things (IoT) based camera networks. However, it is challenging to perform the inference of such resource-hungry DL models on the computationally limited IoT system. Compared to cloud computing, edge computing deploys resources near the end-users to reduce the transmission delay and retains the raw data on the trusted servers to mitigate privacy concerns. Since resources at the edge are limited, management like user association decisions, edge resources allocation, and device configuration with DL model parameter selection becomes essential. This work proposes a coalition formation game-based algorithm to solve the association problem between IoT-based cameras and the edge nodes. Our goal is to maximize the social welfare that consists of multi-view detection enhancement, the privacy retained preference, and the power savings from the cameras. Besides, we adopt the concept of split-ML to provide more flexibility for networking and computing resources allocation at the edge. The final coalition structure is proved to converge and maintain stability. The simulation results show that each design knob, including association decisions made by coalition formation, DNN layer-level partition, and multi-view detection, is essential under different scenario settings.