摘要： 对于计算资源有限的用户设备（UD）而言，处理计算密集型任务是较为困难的。边缘计算通过将计算资源扩展到网络边缘给予了帮助，其关键使能功能之一便是计算任务的合理卸载。如何协调众多边缘节点的计算资源进行任务卸载，且在任务卸载过程中保障数据安全是其重要挑战。因此，提出了一种基于深度强化学习（DRL）的任务安全卸载方法。首先构建了边缘计算网络模型，并为其设计了可变的安全防护机制以适应性的保障数据安全。然后，将边缘计算网络模型和目标进行形式化，并将其进一步转化为马尔可夫决策过程（MDP）。最后，提出了一种基于惩罚动作空间的DRL方法，以给出最优的任务卸载策略。仿真结果表明，所提方案可以在进行安全防护的同时，降低时延和能源消耗成本，且始终保持零任务丢失率。

Abstract: For user devices (UD) with limited computing resources, handling computation-intensive tasks is quite challenging. Edge computing helps by extending computational resources to the network edge, and one of its key enabling functions is the efficient offloading of tasks. Coordinating the computational resources of numerous edge nodes for task offloading, while ensuring data security during the offloading process, is a significant challenge. Therefore, a task security offloading method based on deep reinforcement learning (DRL) is proposed. First, an edge computing network model is constructed, and a variable security protection mechanism is designed to adaptively ensure data security. Then, the edge computing network model and objectives are formalized and further transformed into a Markov decision process (MDP). Finally, a DRL method based on a penalized action space is proposed to derive the optimal task offloading strategy. Simulation results show that the proposed method can reduce latency and energy consumption costs while ensuring security protection, and consistently maintain a zero task loss rate.