FEDBUD联合优化联邦学习中的隐私与激励，兼顾数据量和噪声水平。
FEDBUD: Joint Incentive and Privacy Optimization for Resource-Constrained Federated Learning
- 构建双阶段斯塔克尔伯格博弈模型，协调云端与边缘节点策略。
- 实验显示在真实数据集上性能显著优于现有方法。
- 适合资源受限场景下需兼顾隐私与激励的联邦学习应用。
联邦学习已成为保护隐私和基于边缘的机器学习的流行范式。然而，抵御差分攻击并设计激励机制仍是该领域的重大瓶颈。尽管已有研究关注隐私感知的联邦学习激励机制设计，但很少同时考虑数据量和噪声水平。本文提出一种新型联邦学习系统 FEDBUD，通过综合考虑数据量和噪声水平对激励策略的影响，联合优化隐私与经济因素。在此系统中，云端服务器控制对边缘节点的货币支付，而边缘节点控制可能影响云端模型性能的数据量和噪声水平。为确定双方的相互最优策略，我们建模 FEDBUD 为两阶段斯塔克尔伯格博弈，并利用均值场估计器和虚拟队列推导纳什均衡。在真实数据集上的实验结果证明了 FEDBUD 的卓越性能。
原文摘要 · Abstract (English)
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.
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