Learning-based Big Data Sharing Incentive in Mobile AIGC Networks

TL;DR

This paper proposes a contract-based incentive mechanism using a PPO algorithm to motivate mobile devices to share high-quality data for AIGC model training in edge networks, addressing data quality and sharing challenges.

Contribution

It introduces a QoD metric based on information age and develops a contract theoretic model optimized by PPO to enhance data sharing incentives in mobile AIGC networks.

Findings

PPO-based contracts effectively incentivize data sharing.

The QoD metric accurately measures sensing data quality.

Numerical results validate the model's efficacy and reliability.

Abstract

Rapid advancements in wireless communication have led to a dramatic upsurge in data volumes within mobile edge networks. These substantial data volumes offer opportunities for training Artificial Intelligence-Generated Content (AIGC) models to possess strong prediction and decision-making capabilities. AIGC represents an innovative approach that utilizes sophisticated generative AI algorithms to automatically generate diverse content based on user inputs. Leveraging mobile edge networks, mobile AIGC networks enable customized and real-time AIGC services for users by deploying AIGC models on edge devices. Nonetheless, several challenges hinder the provision of high-quality AIGC services, including issues related to the quality of sensing data for AIGC model training and the establishment of incentives for big data sharing from mobile devices to edge devices amidst information asymmetry. In this paper, we initially define a Quality of Data (QoD) metric based on the age of information to quantify the quality of sensing data. Subsequently, we propose a contract theoretic model aimed at motivating mobile devices for big data sharing. Furthermore, we employ a Proximal Policy Optimization (PPO) algorithm to determine the optimal contract. Numerical results demonstrate the efficacy and reliability of the proposed PPO-based contract model.