XOR Mixup: Privacy-Preserving Data Augmentation for One-Shot Federated Learning

Understand

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.