Privacy-Preserving Incentive Allocation for Fair and Resilient Data Sharing in Resource-Constrained Edge Computing Networks

Yanfang Wang, Shaobo Li (), Kangkun Chen, Ran Guo and Judy Li
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Yanfang Wang: State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, China
Shaobo Li: State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, China
Kangkun Chen: School of Computer Science and Engineering, Central South University, Changsha 410083, China
Ran Guo: School of Computer Science and Engineering, Central South University, Changsha 410083, China
Judy Li: School of Computer Science and Engineering, Central South University, Changsha 410083, China

Mathematics, 2025, vol. 13, issue 3, 1-37

Abstract: Efficient and secure data sharing is paramount for advancing modern digital ecosystems, especially within edge computing environments characterized by resource-constrained nodes and dynamic network topologies. In such settings, privacy preservation, computational efficiency, and system resilience are critical for user engagement and overall system performance. However, existing approaches face three primary challenges: (i) limited optimization of privacy protection and absence of dynamic privacy budget scheduling for resource-constrained scenarios, (ii) static incentive mechanisms that overlook individual differences in data quality and resource consumption, and (iii) inadequate strategies to ensure resilience in environments with limited resources and unstable networks. This paper introduces the Federated Learning-based Dynamic Incentive Allocation Framework (FL-DIAF) to address these issues. FL-DIAF integrates differential privacy into the federated learning paradigm deployed on edge nodes, enabling collaborative model training that safeguards individual data privacy while maintaining computational efficiency and system resilience. Additionally, the framework employs a Shapley value-based dynamic incentive allocation model to ensure equitable and transparent distribution of incentives by accurately quantifying each participant’s contribution within an elastic edge computing infrastructure. Comprehensive experimental evaluations on diverse datasets demonstrate that FL-DIAF achieves a 9.573% reduction in the objective function value under typical conditions and attains a 100% task completion rate across all tested resilient edge scenarios.

Keywords: federated learning; dynamic incentive allocation; privacy protection; resource optimization; Shapley value (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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