fedbud-joint-incentive-privacy-optimization

---
name: fedbud-joint-incentive-privacy-optimization
description: "Federated learning has become a popular paradigm for privacy protection and edge-based machine learning. However, defending against differential attac... 触发词: 联邦学习, 控制系统."
---

# FEDBUD: Joint Incentive and Privacy Optimization for Resource-Constrained Federated Learning

## Overview
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.

## Source Paper
- **Title:** FEDBUD: Joint Incentive and Privacy Optimization for Resource-Constrained Federated Learning
- **Authors:** Tao Liu, Xuehe Wang
- **arXiv:** 2604.10499v1
- **Published:** 2026-04-12

## Core Concepts

1. **联邦学习**
2. **控制系统**
3. **隐私保护**

## Practical Applications

### 实现框架
```python
class Fedbud_Joint_Incentive_Privacy_Optimization:
    def __init__(self):
        self.framework = "fedbud-joint-incentive-privacy-optimization"
        self.source = "arXiv:2604.10499v1"
    
    def apply(self, data):
        """
        应用论文中的方法论
        """
        pass
```

## 方法论要点

1. **理论基础**: 基于论文提出的新方法
2. **实现步骤**: 参考论文算法描述
3. **验证方法**: 与论文实验结果对比

## References
- Tao Liu et al. (2026). arXiv:2604.10499v1
- PDF: https://arxiv.org/pdf/2604.10499v1

## Activation Keywords
- 联邦学习, 控制系统, 隐私保护