论文标题
XOR混音：保存隐私的数据增强，用于联合学习
XOR Mixup: Privacy-Preserving Data Augmentation for One-Shot Federated Learning
论文作者
论文摘要
用户生成的数据分布通常在设备和标签上都会失衡，从而阻碍了联合学习的性能（FL）。为了纠正这种非独立且相同分布的（非IID）数据问题，在这项工作中，我们开发了一种基于隐私的XOR混合数据增强技术，创造了Xormixup，从而提出了一个新颖的单发FL框架，称为Xormixfl。核心想法是收集仅使用每个设备自己的数据示例解码的其他设备的编码数据样本。解码提供了合成但现实的样本，直到诱导用于模型训练的IID数据集。编码和解码过程都遵循有意扭曲原始样本的位XOR操作，从而保留了数据隐私。在非IID MNIST数据集下，Xormixfl的精度比Vanilla FL提高了17.6％的仿真结果。
User-generated data distributions are often imbalanced across devices and labels, hampering the performance of federated learning (FL). To remedy to this non-independent and identically distributed (non-IID) data problem, in this work we develop a privacy-preserving XOR based mixup data augmentation technique, coined XorMixup, and thereby propose a novel one-shot FL framework, termed XorMixFL. The core idea is to collect other devices' encoded data samples that are decoded only using each device's own data samples. The decoding provides synthetic-but-realistic samples until inducing an IID dataset, used for model training. Both encoding and decoding procedures follow the bit-wise XOR operations that intentionally distort raw samples, thereby preserving data privacy. Simulation results corroborate that XorMixFL achieves up to 17.6% higher accuracy than Vanilla FL under a non-IID MNIST dataset.
