利用误导信号辅助的私有多跳分段学习的模型划分与设备任务分配优化

In this paper, deceptive signal-assisted private split learning is investigated. In our model, several edge devices jointly perform collaborative training, and some eavesdroppers aim to collect the model and data information from devices. To prevent the eavesdroppers from collecting model and data information, a subset of devices can transmit deceptive signals. Therefore, it is necessary to determine the subset of devices used for deceptive signal transmission, the subset of model training devices, and the models assigned to each model training device. This problem is formulated as an optimization problem whose goal is to minimize the information leaked to eavesdroppers while meeting the model training energy consumption and delay constraints. To solve this problem, we propose a soft actor-critic deep reinforcement learning framework with intrinsic curiosity module and cross-attention (ICM-CA) that enables a centralized agent to determine the model training devices, the deceptive signal transmission devices, the transmit power, and sub-models assigned to each model training device without knowing the position and monitoring probability of eavesdroppers. The proposed method uses an ICM module to encourage the server to explore novel actions and states and a CA module to determine the importance of each historical state-action pair thus improving training efficiency. Simulation results demonstrate that the proposed method improves the convergence rate by up to 3x and reduces the information leaked to eavesdroppers by up to 13% compared to the traditional SAC algorithm. 我们在本研究中探讨了利用误导信号辅助的私有分段学习。在我们的模型中，多个边缘设备联合执行协作训练，一些窃听者试图收集设备上的模型和数据信息。为防止窃听者获取模型和数据信息，部分设备可传输误导信号。因此，必须确定用于误导信号传输的设备子集、用于模型训练的设备子集以及分配给每个模型训练设备的模型。这一问题被形式化为一个优化问题，其目标是在满足模型训练能源消耗和延迟约束的前提下，最小化传输给窃听者的信息泄露。为解决该问题，我们提出了一种基于内在好奇心模块和跨注意力（ICM-CA）的软演员-评论家深度强化学习框架，使集中代理能够在不知道窃听者位置和监控概率的情况下，确定模型训练设备、误导信号传输设备、传输功率以及分配给每个模型训练设备的子模型。该方法使用 ICM 模块鼓励服务器探索新的动作和状态，并使用 CA 模块确定每个历史状态-动作对的重要性，从而提高训练效率。仿真结果表明，所提出的方法相较于传统 SAC 算法，将收敛速度提高最高可达 3 倍，将传输给窃听者的信息减少最高可达 13%。