Motivating learners in multiorchestrator mobile edge learning: A stackelberg game approach

Document Type

Article

Publication Title

IEEE Canadian Journal of Electrical and Computer Engineering

Abstract

Mobile edge learning (MEL) is a learning paradigm that enables distributed training of machine learning (ML) models over heterogeneous edge devices (e.g., IoT devices). Multiorchestrator MEL refers to the coexistence of multiple learning tasks with different datasets, each of which being governed by an orchestrator to facilitate the distributed training process. In MEL, the training performance deteriorates without the availability of sufficient training data or computing resources. Therefore, it is crucial to motivate edge devices to become learners and offer their computing resources, and either offer their private data or receive the needed data from the orchestrator and participate in the training process of a learning task. In this work, we propose an incentive mechanism, where we formulate the orchestrators-learners interactions as a 2-round Stackelberg game to motivate the participation of the learners. In the first round, the learners decide which learning task to get engaged in, and then in the second round, the training parameters and the amount of data for training in case of participation such that their utility is maximized. We then study the training round analytically and derive the learners optimal strategy. Finally, numerical experiments have been conducted to evaluate the performance of the proposed incentive mechanism.

First Page

69

Last Page

76

DOI

10.1109/ICJECE.2022.3206393

Publication Date

10-12-2022

Keywords

Distributed learning, Edge learning, Edge networks, Game theory, Stackelberg game

Comments

IR conditions: non-described

Link to Full Text

Share

COinS