FEDBUD: Joint Incentive and Privacy Optimization for Resource-Constrained Federated Learning
For federated learning systems, this work addresses the underexplored joint optimization of incentive and privacy under resource constraints.
FEDBUD jointly optimizes incentive and privacy in federated learning by modeling a two-stage Stackelberg Game that considers data volume and noise level, achieving Nash Equilibrium via mean-field estimator and virtual queue. Experiments show outstanding performance on real-world datasets.
Federated learning has become a popular paradigm for privacy protection and edge-based machine learning. However, defending against differential attacks and devising incentive strategies remain significant bottlenecks in this field. Despite recent works on privacy-aware incentive mechanism design for federated learning, few of them consider both data volume and noise level. In this paper, we propose a novel federated learning system called FEDBUD, which combines privacy and economic concerns together by considering the joint influence of data volume and noise level on incentive strategy determination. In this system, the cloud server controls monetary payments to edge nodes, while edge nodes control data volume and noise level that potentially impact the model performance of the cloud server. To determine the mutually optimal strategies for both sides, we model FEDBUD as a two-stage Stackelberg Game and derive the Nash Equilibrium using the mean-field estimator and virtual queue. Experimental results on real-world datasets demonstrate the outstanding performance of FEDBUD.