Mobile edge computing (MEC) is a promising solution for enhancing the user experience, minimizing content delivery expenses, and reducing backhaul traffic. In this paper, we propose a novel privacy-preserving decentralized game-theoretic framework for resource crowdsourcing in MEC. Our framework models the interactions between a content provider (CP) and multiple mobile edge device users (MEDs) as a non-cooperative game, in which MEDs offer idle storage resources for content caching in exchange for rewards. We introduce efficient decentralized gradient play algorithms for Nash equilibrium (NE) computation by exchanging local information among neighboring MEDs only, thus preventing attackers from learning users' private information. The key challenge in designing such algorithms is that communication among MEDs is not fixed and is facilitated by a sequence of undirected time-varying graphs. Our approach achieves linear convergence to the NE without imposing any assumptions on the values of parameters in the local objective functions, such as requiring strong monotonicity to be stronger than its dependence on other MEDs' actions, which is commonly required in existing literature when the graph is directed time-varying. Extensive simulations demonstrate the effectiveness of our approach in achieving efficient resource outsourcing decisions while preserving the privacy of the edge devices.

翻译：移动边缘计算（MEC）是提升用户体验、降低内容交付成本并减少回程流量的有效方案。本文提出了一种新颖的保护隐私的去中心化博弈论框架，用于MEC中的资源众包。该框架将内容提供商（CP）与多个移动边缘设备用户（MED）之间的交互建模为非合作博弈，其中MED通过提供空闲存储资源进行内容缓存以获取奖励。我们引入了高效的迭代去中心化梯度算法，仅通过相邻MED之间的局部信息交换即可计算纳什均衡（NE），从而防止攻击者获取用户的隐私信息。设计此类算法的关键挑战在于：MED间的通信并非固定不变的，而是通过一系列无向时变图来实现。我们的方法在线性收敛速度下达到NE，且无需对局部目标函数中的参数值施加任何假设（例如要求强单调性必须强于对其他MED行为的依赖性），而这一假设在现有文献中针对有向时变图场景通常是必需的。大量仿真实验表明，所提方法能够在实现高效资源外包决策的同时保护边缘设备的隐私。