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

arXiv 2006.05148 ↗First posted 9 Jun 2020cs.LG cs.CR eess.SPcited by 58

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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.

Key facts. Cited by 58 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Jun 2020.

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Citing papers per year: 2021: 5; 2022: 14; 2023: 12; 2024: 16; 2025: 9; 2026: 2.

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  1. 2021Federated Learning on Non-IID Data: A Surveycited by 345
  2. 2023Heterogeneous Federated Learning: State-of-the-art and Research Challengescited by 160
  3. 2021Local Learning Matters: Rethinking Data Heterogeneity in Federated Learningcited by 114
  4. 2021FedMix: Approximation of Mixup under Mean Augmented Federated Learningcited by 108
  5. 2023Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmarkcited by 97
  6. 2023FedFed: Feature Distillation against Data Heterogeneity in Federated Learningcited by 54
  7. 2022Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learningcited by 52
  8. 2022Preserving Privacy in Federated Learning with Ensemble Cross-Domain Knowledge Distillationcited by 45
  9. 2022FedBR: Improving Federated Learning on Heterogeneous Data via Local Learning Bias Reductioncited by 35
  10. 2022A Survey of Mix-based Data Augmentation: Taxonomy, Methods, Applications, and Explainabilitycited by 30
  11. 2024FeDeRA:Efficient Fine-tuning of Language Models in Federated Learning Leveraging Weight Decompositioncited by 26
  12. 2021Survey: Image Mixing and Deleting for Data Augmentationcited by 25
38 more
  1. 2022Federated Learning with GAN-based Data Synthesis for Non-IID Clientscited by 25
  2. 2023Federated Domain Generalization: A Surveycited by 25
  3. 2022DYNAFED: Tackling Client Data Heterogeneity with Global Dynamicscited by 22
  4. 2023FedLPA: One-shot Federated Learning with Layer-Wise Posterior Aggregationcited by 22
  5. 2023A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiencycited by 21
  6. 2022One-shot Federated Learning without Server-side Trainingcited by 20
  7. 2024A Survey on Mixup Augmentations and Beyondcited by 18
  8. 2024FedBiP: Heterogeneous One-Shot Federated Learning with Personalized Latent Diffusion Modelscited by 16
  9. 2024FedImpro: Measuring and Improving Client Update in Federated Learningcited by 15
  10. 2024FedFisher: Leveraging Fisher Information for One-Shot Federated Learningcited by 15
  11. 2023Feature Matching Data Synthesis for Non-IID Federated Learningcited by 13
  12. 2024Revisiting Ensembling in One-Shot Federated Learningcited by 13
  13. 2021Federated Few-Shot Learning with Adversarial Learningcited by 11
  14. 2024FuseFL: One-Shot Federated Learning through the Lens of Causality with Progressive Model Fusioncited by 11
  15. 2025Geometric Knowledge-Guided Localized Global Distribution Alignment for Federated Learningcited by 11
  16. 2024Stable Diffusion-based Data Augmentation for Federated Learning with Non-IID Datacited by 10
  17. 2023FedDWA: Personalized Federated Learning with Dynamic Weight Adjustmentcited by 9
  18. 2022An Adaptive Federated Relevance Framework for Spatial Temporal Graph Learningcited by 7
  19. 2022Federated Learning with Privacy-Preserving Ensemble Attention Distillationcited by 6
  20. 2025One-shot Federated Learning Methods: A Practical Guidecited by 6
  21. 2025Subgraph Federated Learning for Local Generalizationcited by 6
  22. 2022FedSPLIT: One-Shot Federated Recommendation System Based on Non-negative Joint Matrix Factorization and Knowledge Distillationcited by 4
  23. 2022FedILC: Weighted Geometric Mean and Invariant Gradient Covariance for Federated Learning on Non-IID Datacited by 4
  24. 2022StatMix: Data augmentation method that relies on image statistics in federated learningcited by 4
  25. 2025From Centralized to Decentralized Federated Learning: Theoretical Insights, Privacy Preservation, and Robustness Challengescited by 4
  26. 2022Robust Federated Learning against both Data Heterogeneity and Poisoning Attack via Aggregation Optimizationcited by 3
  27. 2024A Unified Solution to Diverse Heterogeneities in One-shot Federated Learningcited by 3
  28. 2023Vicinal Feature Statistics Augmentation for Federated 3D Medical Volume Segmentationcited by 2
  29. 2023Privacy and Accuracy Implications of Model Complexity and Integration in Heterogeneous Federated Learningcited by 2
  30. 2024DFDG: Data-Free Dual-Generator Adversarial Distillation for One-Shot Federated Learningcited by 2
  31. 2025pMixFed: Efficient Personalized Federated Learning through Adaptive Layer-Wise Mixupcited by 2
  32. 2025FedBM: Stealing Knowledge from Pre-trained Language Models for Heterogeneous Federated Learningcited by 2
  33. 2025Towards One-shot Federated Learning: Advances, Challenges, and Future Directionscited by 2
  34. 2024PARMESAN: Parameter-Free Memory Search and Transduction for Dense Prediction Taskscited by 1
  35. 2024Concurrent vertical and horizontal federated learning with fuzzy cognitive mapscited by 1
  36. 2025TOFA: Training-Free One-Shot Federated Adaptation for Vision-Language Modelscited by 1
  37. 2022Mitigating Data Absence in Federated Learning Using Privacy-Controllable Data Digestscited by 0
  38. 2023Federated Semi-Supervised Learning with Annotation Heterogeneitycited by 0

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