论文标题
在饥饿的联合数据下，强大的智能家庭脸识别
Robust Smart Home Face Recognition under Starving Federated Data
论文作者
论文摘要
在过去的几年中，对抗性攻击领域受到了各种研究人员的众多关注，并在成功的攻击成功率上针对知名的深层神经网络，这些神经网络被公认为在各种任务中获得高分类能力。但是，大多数实验都是在单个模型下完成的，我们认为这在现实生活中可能不是理想的情况。在本文中，我们介绍了一种新颖的联邦对抗训练方法，用于智能家庭面部识别，名为Flats，我们观察到了一些有趣的发现，这些发现可能在传统的对抗性攻击中对联邦学习实验的传统攻击可能不容易注意到。通过对超参数应用不同的变化，我们发现我们的方法可以使全球模型在饥饿的联合环境中变得强大。我们的代码可以在https://github.com/jcroh0508/flats上找到。
Over the past few years, the field of adversarial attack received numerous attention from various researchers with the help of successful attack success rate against well-known deep neural networks that were acknowledged to achieve high classification ability in various tasks. However, majority of the experiments were completed under a single model, which we believe it may not be an ideal case in a real-life situation. In this paper, we introduce a novel federated adversarial training method for smart home face recognition, named FLATS, where we observed some interesting findings that may not be easily noticed in a traditional adversarial attack to federated learning experiments. By applying different variations to the hyperparameters, we have spotted that our method can make the global model to be robust given a starving federated environment. Our code can be found on https://github.com/jcroh0508/FLATS.
