mlsanityData to 7 Oct 2026

StatMix: Data augmentation method that relies on image statistics in federated learning

arXiv 2207.04103 ↗First posted 8 Jul 2022cs.LG cs.CVcited by 4

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Abstract (arXiv)

Availability of large amount of annotated data is one of the pillars of deep learning success. Although numerous big datasets have been made available for research, this is often not the case in real life applications (e.g. companies are not able to share data due to GDPR or concerns related to intellectual property rights protection). Federated learning (FL) is a potential solution to this problem, as it enables training a global model on data scattered across multiple nodes, without sharing local data itself. However, even FL methods pose a threat to data privacy, if not handled properly. Therefore, we propose StatMix, an augmentation approach that uses image statistics, to improve results of FL scenario(s). StatMix is empirically tested on CIFAR-10 and CIFAR-100, using two neural network architectures. In all FL experiments, application of StatMix improves the average accuracy, compared to the baseline training (with no use of StatMix). Some improvement can also be observed in non-FL setups.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 23 of its 30 references that are in the map. First posted on arXiv 8 Jul 2022.

Builds on

23 of 30 references.

References · 23

  1. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  3. 2017mixup: Beyond Empirical Risk Minimizationcited by 5,155
  4. 2016SGDR: Stochastic Gradient Descent with Warm Restartscited by 4,993
  5. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  6. 2016Identity Mappings in Deep Residual Networkscited by 1,960
  7. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  8. 2018Federated Learning with Non-IID Datacited by 1,099
  9. 2016An overview of gradient descent optimization algorithmscited by 1,021
  10. 2021Domain Generalization with MixStylecited by 563
  11. 2016Federated Optimization: Distributed Machine Learning for On-Device Intelligencecited by 558
  12. 2017Differentially Private Federated Learning: A Client Level Perspectivecited by 451
11 more
  1. 2017Deep Layer Aggregationcited by 436
  2. 2019Bayesian Nonparametric Federated Learning of Neural Networkscited by 385
  3. 2019A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protectioncited by 371
  4. 2019The Non-IID Data Quagmire of Decentralized Machine Learningcited by 303
  5. 2018Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Datacited by 242
  6. 2021FedMix: Approximation of Mixup under Mean Augmented Federated Learningcited by 108
  7. 2021Towards Fair Federated Learning with Zero-Shot Data Augmentationcited by 64
  8. 2020XOR Mixup: Privacy-Preserving Data Augmentation for One-Shot Federated Learningcited by 58
  9. 2020Mix2FLD: Downlink Federated Learning After Uplink Federated Distillation With Two-Way Mixupcited by 29
  10. 2021An overview of mixing augmentation methods and augmentation strategiescited by 14
  11. 2022Using adversarial images to improve outcomes of federated learning for non-IID datacited by 1

Built on by

Cited by year

Citing papers per year: 2023: 1; 2024: 2; 2025: 1.

4 papers.

  1. 2023Federated Domain Generalization: A Surveycited by 25
  2. 2024A Survey on Mixup Augmentations and Beyondcited by 18
  3. 2024Advances in Robust Federated Learning: A Survey with Heterogeneity Considerationscited by 12
  4. 2025Gains: Fine-grained Federated Domain Adaptation in Open Setcited by 0