Secure Computation Offloading for Device-Collaborative MEC Networks: A DRL-Based Approach

This paper investigates secure computation offloading in device-collaborative mobile edge computing (MEC) networks, where mobile users (MUs) can process a part of their tasks locally and offload the other part to the MEC server or other idle paired MUs. All the transmission links are exposed to a potential eavesdropper. To prevent information leakage, artificial noise (AN) is employed and the secrecy capacity is considered. For such a system, we formulate a weighted latency-energy-aware cost minimization problem by jointly optimizing the subchannel allocation, the offloading proportions, the MUs' transmit powers, the MUs' and the MEC server's CPU frequencies, and the AN power while guaranteeing the secrecy capacities of all transmission links. Since the variables are multi-dimensional and coupled, the formulated problem is challenging to solve with the traditional optimization method. We first propose a swap-matching-based algorithm to achieve the optimal subchannel allocation. We then propose a two-layer asynchronous advantage actor-critic (A3C) algorithm to optimize the rest of the resource variables. Specifically, in the inner layer, by utilizing the Karush-Kuhn-Tucker conditions, we derive the optimal CPU frequency with semi-closed-form expressions. In the outer layer, we propose an A3C-based algorithm to achieve near-optimal solutions with fast convergence speed based on the obtained optimal CPU frequency. Simulation results demonstrate that our proposed scheme can decrease the latency-energy-aware cost compared with other learning-based algorithms.