论文标题
高空联盟学习，并增强了隐私
Over-the-Air Federated Learning with Enhanced Privacy
论文作者
论文摘要
联合学习（FL）已成为一种有希望的学习范式，其中仅共享本地模型参数（梯度）。私人用户数据永远不会离开本地设备，从而保留数据隐私。但是，最近的研究表明，即使用户从未共享本地数据，在没有保护的情况下交换模型参数也会泄漏私人信息。此外，在无线系统中，模型参数的频繁传输可能会在模型较大时引起巨大的带宽消耗和网络拥塞。为了解决这个问题，我们提出了一个新的FL框架，具有有效的空中参数聚合和对用户数据和模型的强大隐私保护。我们通过在最终设备上引入成对取消的随机人造噪声（PCR-ans）来实现这一目标。与现有的空中计算（AIRCOMP）FL计划相比，我们的设计提供了更强的隐私保护。我们通过分析显示了所提出的无线FL聚合算法的保密能力和收敛速率。
Federated learning (FL) has emerged as a promising learning paradigm in which only local model parameters (gradients) are shared. Private user data never leaves the local devices thus preserving data privacy. However, recent research has shown that even when local data is never shared by a user, exchanging model parameters without protection can also leak private information. Moreover, in wireless systems, the frequent transmission of model parameters can cause tremendous bandwidth consumption and network congestion when the model is large. To address this problem, we propose a new FL framework with efficient over-the-air parameter aggregation and strong privacy protection of both user data and models. We achieve this by introducing pairwise cancellable random artificial noises (PCR-ANs) on end devices. As compared to existing over-the-air computation (AirComp) based FL schemes, our design provides stronger privacy protection. We analytically show the secrecy capacity and the convergence rate of the proposed wireless FL aggregation algorithm.
