In recent years, data privacy and security have attracted increasing attention in the age of artificial intelligence. Although federated learning (FL) can avoid data leakage by only sharing the machine learning models, it still suffers from differential attacks which erode the privacy of data owners. In wireless networks, the inherent channel noise can be utilized for differential privacy (DP) protection. However, the problem of incentivizing mobile devices, i.e., data owners, with the demand for DP protection to complete FL tasks has received limited attention so far. In this letter, we establish a system model for DP preserving wireless federated learning and propose an incentive mechanism based on the Stackelberg game. Our theoretical proof and numerical results demonstrate that the proposed game model can achieve the Nash equilibrium and the superior performance in maximizing the server’s utility.

中文翻译：

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一种具有差异隐私的无线联合学习的 Stackelberg 激励机制

近年来，在人工智能时代，数据隐私和安全受到越来越多的关注。虽然联邦学习（FL）可以通过仅共享机器学习模型来避免数据泄露，但它仍然遭受着侵蚀数据所有者隐私的差分攻击。在无线网络中，固有的信道噪声可用于差分隐私（DP）保护。然而，到目前为止，激励移动设备（即数据所有者）对 DP 保护的需求来完成 FL 任务的问题受到的关注有限。在这封信中，我们建立了一个保留DP的无线联邦学习系统模型，并提出了一种基于Stackelberg博弈的激励机制。