Security Enhanced Computation Offloading for Collaborative Inference at Semantic-Communication-Empowered Edge

Semantic communication (SC) has emerged as a promising paradigm for upcoming intelligent applications, enabling mobile devices to collaboratively execute intelligent tasks with edge servers through computation offloading. However, few studies have addressed the problem of collaborative inference in SC networks. Traditional collaborative inference mechanisms may suffer performance decline in SC systems and are vulnerable to eavesdroppers. To address these issues, first, we present an encryptor that encrypts semantic information to avoid privacy leakage and a decryptor for restoration. Besides, we propose a novel SC-empowered edge computing framework enabling mobile devices to deploy a partial semantic encoder and offload the rest to edge servers. Based on this framework, we formulate the collaborative inference optimization problem, jointly optimizing delay, energy consumption, and privacy leakage. DNNPart is devised based on deep deterministic policy gradient to address the problem, which consists of a semantic attention mechanism that enables it to focus on important state variables, a hybrid action representation method that makes it adapt to mixed discrete and continuous action spaces, a dynamic model splitting algorithm that locates the optimal partition layer and adaptively splits the semantic coders. Integrated with these components, DNNPart iteratively optimizes the offloading strategy to find the optimal offloading strategy. Extensive simulations were conducted to verify the effectiveness of the proposed method by comparing it with baseline mechanisms.