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

arXiv: https://arxiv.org/abs/2006.05148 · HTML: https://mlsanity.com/paper/2006.05148/

## Key facts

- Cited by 58 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jun 2020.

## Built on by (most cited first, 30 of 58)

- [Federated Learning on Non-IID Data: A Survey](https://mlsanity.com/paper/2106.06843/) (2021)
- [Heterogeneous Federated Learning: State-of-the-art and Research Challenges](https://mlsanity.com/paper/2307.10616/) (2023)
- [Local Learning Matters: Rethinking Data Heterogeneity in Federated Learning](https://mlsanity.com/paper/2111.14213/) (2021)
- [FedMix: Approximation of Mixup under Mean Augmented Federated Learning](https://mlsanity.com/paper/2107.00233/) (2021)
- [Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmark](https://mlsanity.com/paper/2311.06750/) (2023)
- [FedFed: Feature Distillation against Data Heterogeneity in Federated Learning](https://mlsanity.com/paper/2310.05077/) (2023)
- [Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learning](https://mlsanity.com/paper/2206.02465/) (2022)
- [Preserving Privacy in Federated Learning with Ensemble Cross-Domain Knowledge Distillation](https://mlsanity.com/paper/2209.04599/) (2022)
- [FedBR: Improving Federated Learning on Heterogeneous Data via Local Learning Bias Reduction](https://mlsanity.com/paper/2205.13462/) (2022)
- [A Survey of Mix-based Data Augmentation: Taxonomy, Methods, Applications, and Explainability](https://mlsanity.com/paper/2212.10888/) (2022)
- [Survey: Image Mixing and Deleting for Data Augmentation](https://mlsanity.com/paper/2106.07085/) (2021)
- [FeDeRA:Efficient Fine-tuning of Language Models in Federated Learning Leveraging Weight Decomposition](https://mlsanity.com/paper/2404.18848/) (2024)
- [Federated Learning with GAN-based Data Synthesis for Non-IID Clients](https://mlsanity.com/paper/2206.05507/) (2022)
- [Federated Domain Generalization: A Survey](https://mlsanity.com/paper/2306.01334/) (2023)
- [DYNAFED: Tackling Client Data Heterogeneity with Global Dynamics](https://mlsanity.com/paper/2211.10878/) (2022)
- [FedLPA: One-shot Federated Learning with Layer-Wise Posterior Aggregation](https://mlsanity.com/paper/2310.00339/) (2023)
- [A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiency](https://mlsanity.com/paper/2307.10655/) (2023)
- [One-shot Federated Learning without Server-side Training](https://mlsanity.com/paper/2204.12493/) (2022)
- [A Survey on Mixup Augmentations and Beyond](https://mlsanity.com/paper/2409.05202/) (2024)
- [FedBiP: Heterogeneous One-Shot Federated Learning with Personalized Latent Diffusion Models](https://mlsanity.com/paper/2410.04810/) (2024)
- [FedImpro: Measuring and Improving Client Update in Federated Learning](https://mlsanity.com/paper/2402.07011/) (2024)
- [FedFisher: Leveraging Fisher Information for One-Shot Federated Learning](https://mlsanity.com/paper/2403.12329/) (2024)
- [Feature Matching Data Synthesis for Non-IID Federated Learning](https://mlsanity.com/paper/2308.04761/) (2023)
- [Revisiting Ensembling in One-Shot Federated Learning](https://mlsanity.com/paper/2411.07182/) (2024)
- [Geometric Knowledge-Guided Localized Global Distribution Alignment for Federated Learning](https://mlsanity.com/paper/2503.06457/) (2025)
- [Federated Few-Shot Learning with Adversarial Learning](https://mlsanity.com/paper/2104.00365/) (2021)
- [Stable Diffusion-based Data Augmentation for Federated Learning with Non-IID Data](https://mlsanity.com/paper/2405.07925/) (2024)
- [FuseFL: One-Shot Federated Learning through the Lens of Causality with Progressive Model Fusion](https://mlsanity.com/paper/2410.20380/) (2024)
- [FedDWA: Personalized Federated Learning with Dynamic Weight Adjustment](https://mlsanity.com/paper/2305.06124/) (2023)
- [An Adaptive Federated Relevance Framework for Spatial Temporal Graph Learning](https://mlsanity.com/paper/2206.03420/) (2022)

## Abstract (arXiv)

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.