Market Analysis of Distributed Learning Resource Management for Internet of Things: A Game-Theoretic Approach

Abstract

In this article, to meet a delay requirement for data analytics from Internet-of-Things (IoT) devices, we design a novel market model of the distributed learning resource management mechanism for multiple mobile-edge computing (MEC) operators. We consider a hybrid architecture (cloud-MEC) for the distributed learning, which is also known as "federated learning" as one of the practical examples, where a coordinator at the cloud coordinates IoT sensors to efficiently distribute their sensing data over multiple MECs. In this sense, multiple MECs receive a shared model from the coordinator and conduct local training of received partial sensing data from IoT sensors. Then, the coordinator at the cloud merges returned local training results from MECs and generates a global model. To model a hierarchical decision-making structure as a market behavior, we formulate and solve a Stackelberg game model. Specifically, in the case of MEC operators as leaders, we design a pricing scheme for MEC operators to obtain its maximum utility by considering a tradeoff between the revenue and energy consumption. Then, as a follower, while the coordinator aims at achieving a balance between the satisfaction attained from the distributed learning and the costs, it follows the MEC operators' decisions by coordinating IoT sensors to distribute their sensing data over MECs. A unique Stackelberg equilibrium (SE) point is given as a closed form. Finally, we reveal that the SE solution maximizes the utility of all market participants. This game-theoretic study demonstrates that there is an incentive for utilizing multiple MECs to achieve better satisfaction of IoT services in distributed learning.