S2E-DECI: Secrecy and Energy-Efficient Dual-Aware Device-Edge Co-Inference for AIoT

This article proposes a secrecy and energy-efficient device-edge co-inference scheme for resource-constrained Artificial Intelligence of Things (AIoT) devices with physical layer security assistance. Our approach leverages split learning, where the AIoT device executes the initial part of the AI model, and the mobile edge computing server (MECs) computes the remainder, reducing energy consumption (EC) and inference delay. We measure secrecy capacity under the finite blocklength regime to address the vulnerability of intermediate feature data (IFD) to eavesdropping over wireless channels and its short block length characteristics. The objective is to minimize the average EC of the device-edge co-inference by jointly optimizing deep neural network (DNN) model partitioning and resource allocation. We formulate a distributed reinforcement learning-based joint DNN model partitioning and resource allocation (DRPA) algorithm, which uses knowledge-based reinforcement learning for optimal DNN partitioning and a convex optimization approach for resource allocation. Simulation results demonstrate that the DRPA algorithm achieves near-optimal performance, closely matching the results of exhaustive search methods.