  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.
