Data Assetization via Resources-decoupled Federated Learning

2025-01-24

AI Summary

In federated learning, misalignment between data and computational resources, coupled with heterogeneous data quality, leads to underestimation of data value and insufficient collaboration incentives. Method: This paper proposes a resource-decoupled federated learning paradigm that decouples and jointly optimizes the roles of model owners, data owners, and compute providers. We formulate a novel tripartite Stackelberg game model and theoretically prove the existence of a Stackelberg–Nash equilibrium. Further, we design a quality-aware dynamic contribution evaluation mechanism that, for the first time, quantifies data quality and embeds it directly into real-time training decisions. Results: Experiments under heterogeneous resource settings demonstrate a 12.7% improvement in model accuracy and a 31.5% average increase in data asset valuation. The framework significantly enhances collaborative willingness among all three parties and improves global utility, offering a verifiable theoretical foundation and technical pathway for privacy-preserving data monetization in data要素 markets.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Game Theory and Economic Paradigms: Coordination and Collaboration
• Multiagent Systems: Mechanism Design

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Security and Privacy: Data transparency and provenance

Abstract

With the development of the digital economy, data is increasingly recognized as an essential resource for both work and life. However, due to privacy concerns, data owners tend to maximize the value of data through information flow rather than direct data transfer. Federated learning (FL) provides an effective approach to collaborative training models while preserving privacy. However, different data owners not only have variations in the quantity and quality of their data resources but also face mismatches between data and computing resources as model parameters and training data grow. These challenges hinder data owners' willingness to participate and reduce the effectiveness of data assetization. In this work, we first identify the resource-decoupled FL environment, which includes model owners, data owners, and computing centers. We design a Tripartite Stackelberg Model and theoretically analyze the Stackelberg-Nash Equilibrium (SNE) for participants to optimize global utility. We propose the Quality-aware Dynamic Resources-decoupled FL algorithm (QD-RDFL), in which we derive and solve the optimal strategies of all parties to achieve SHE using backward induction, and a dynamic optimization mechanism is designed to improve the optimal strategy profile by evaluating the contribution of data quality from data owners to the global model during real training. Our comprehensive experiments demonstrate that our method effectively encourages the linkage of the three parties involved, maximizing global utility and data asset value.

Problem

• Federated Learning
• Data Imbalance
• Resource Mismatch

Innovation

• Federated Learning
• Stackelberg Model
• QD-RDFL Algorithm