Incentive Mechanism Design for Resource Sharing in Collaborative Edge Learning

TL;DR

This paper explores incentive mechanisms for resource sharing in collaborative edge learning, proposing an auction-based approach to motivate heterogeneous edge devices to contribute their resources effectively.

Contribution

It introduces a novel incentive mechanism framework for resource sharing in edge learning, including a deep learning-based auction design for pricing contributed data.

Findings

The proposed auction maximizes revenue compared to benchmarks.

Resource heterogeneity poses challenges addressed by the incentive scheme.

Deep learning enhances auction performance for edge resource valuation.

Abstract

In 5G and Beyond networks, Artificial Intelligence applications are expected to be increasingly ubiquitous. This necessitates a paradigm shift from the current cloud-centric model training approach to the Edge Computing based collaborative learning scheme known as edge learning, in which model training is executed at the edge of the network. In this article, we first introduce the principles and technologies of collaborative edge learning. Then, we establish that a successful, scalable implementation of edge learning requires the communication, caching, computation, and learning resources (3C-L) of end devices and edge servers to be leveraged jointly in an efficient manner. However, users may not consent to contribute their resources without receiving adequate compensation. In consideration of the heterogeneity of edge nodes, e.g., in terms of available computation resources, we discuss the challenges of incentive mechanism design to facilitate resource sharing for edge learning. Furthermore, we present a case study involving optimal auction design using Deep Learning to price fresh data contributed for edge learning. The performance evaluation shows the revenue maximizing properties of our proposed auction over the benchmark schemes.