Incentive-based Resource Allocation for Mobile Edge Learning

Abstract

Mobile Edge Learning (MEL) is a learning paradigm that facilitates training of Machine Learning (ML) models over resource-constrained edge devices. MEL consists of an orchestrator, which represents the model owner of the learning task, and learners, which own the data locally. Enabling the learning process requires the model owner to motivate learners to train the ML model on their local data and allocate sufficient resources. The time limitations and the possible existence of multiple orchestrators open the doors for the resource allocation problem. As such, we model the incentive mechanism and resource allocation as a multi-round Stackelberg game, and propose a Payment-based Time Allocation (PBTA) algorithm to solve the game. In PBTA, orchestrators first determine the pricing, then the learners allocate each orchestrator a timeslot and determine the amount of data and resources for each orchestrator. Finally, we evaluate the PBTA performance and compare it against a recent state-of-the-art approach.