mlsanityData to 7 Oct 2026

Federated Learning with GAN-based Data Synthesis for Non-IID Clients

arXiv 2206.05507 ↗First posted 11 Jun 2022cs.LGcited by 25

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Abstract (arXiv)

Federated learning (FL) has recently emerged as a popular privacy-preserving collaborative learning paradigm. However, it suffers from the non-independent and identically distributed (non-IID) data among clients. In this paper, we propose a novel framework, named Synthetic Data Aided Federated Learning (SDA-FL), to resolve this non-IID challenge by sharing synthetic data. Specifically, each client pretrains a local generative adversarial network (GAN) to generate differentially private synthetic data, which are uploaded to the parameter server (PS) to construct a global shared synthetic dataset. To generate confident pseudo labels for the synthetic dataset, we also propose an iterative pseudo labeling mechanism performed by the PS. A combination of the local private dataset and synthetic dataset with confident pseudo labels leads to nearly identical data distributions among clients, which improves the consistency among local models and benefits the global aggregation. Extensive experiments evidence that the proposed framework outperforms the baseline methods by a large margin in several benchmark datasets under both the supervised and semi-supervised settings.

Key facts. Cited by 25 papers in the map (data through 7 Oct 2026). Builds on 26 of its 48 references that are in the map. First posted on arXiv 11 Jun 2022.

Builds on

26 of 48 references.

References · 26

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  3. 2017mixup: Beyond Empirical Risk Minimizationcited by 5,155
  4. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,283
  5. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  6. 2017Improved Training of Wasserstein GANscited by 2,223
  7. 2020FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidencecited by 2,080
  8. 2018Federated Learning with Non-IID Datacited by 1,099
  9. 2020Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimizationcited by 897
  10. 2021Federated Learning on Non-IID Data Silos: An Experimental Studycited by 566
  11. 2016Conditional Image Synthesis With Auxiliary Classifier GANscited by 562
  12. 2019Federated Learning with Personalization Layerscited by 533
14 more
  1. 2020An Efficient Framework for Clustered Federated Learningcited by 505
  2. 2021Federated Learning on Non-IID Data: A Surveycited by 345
  3. 2020Think Locally, Act Globally: Federated Learning with Local and Global Representationscited by 322
  4. 2020COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Imagescited by 264
  5. 2018Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Datacited by 242
  6. 2021No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Datacited by 241
  7. 2018Differentially Private Generative Adversarial Networkcited by 235
  8. 2020Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learningcited by 112
  9. 2021FedMix: Approximation of Mixup under Mean Augmented Federated Learningcited by 108
  10. 2020XOR Mixup: Privacy-Preserving Data Augmentation for One-Shot Federated Learningcited by 58
  11. 2019Hybrid-FL for Wireless Networks: Cooperative Learning Mechanism Using Non-IID Datacited by 38
  12. 2020Mix2FLD: Downlink Federated Learning After Uplink Federated Distillation With Two-Way Mixupcited by 29
  13. 2021FedDPGAN: Federated Differentially Private Generative Adversarial Networks Framework for the Detection of COVID-19 Pneumoniacited by 22
  14. 2020FedCD: Improving Performance in non-IID Federated Learningcited by 13

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 11; 2024: 7; 2025: 4; 2026: 1.

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  1. 2022FedFA: Federated Learning with Feature Anchors to Align Features and Classifiers for Heterogeneous Datacited by 49
  2. 2023Phoenix: A Federated Generative Diffusion Modelcited by 21
  3. 2023A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiencycited by 21
  4. 2022DReS-FL: Dropout-Resilient Secure Federated Learning for Non-IID Clients via Secret Data Sharingcited by 17
  5. 2024FedBiP: Heterogeneous One-Shot Federated Learning with Personalized Latent Diffusion Modelscited by 16
  6. 2023Feature Matching Data Synthesis for Non-IID Federated Learningcited by 13
  7. 2023FedCiR: Client-Invariant Representation Learning for Federated Non-IID Featurescited by 12
  8. 2024Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directionscited by 12
  9. 2023Understanding and Improving Model Averaging in Federated Learning on Heterogeneous Datacited by 7
  10. 2024A Systematic Review of Federated Generative Modelscited by 5
  11. 2023PS-FedGAN: An Efficient Federated Learning Framework Based on Partially Shared Generative Adversarial Networks For Data Privacycited by 4
  12. 2025Towards Harnessing the Collaborative Power of Large and Small Models for Domain Taskscited by 4
13 more
  1. 2023Towards Open Federated Learning Platforms: Survey and Vision from Technical and Legal Perspectivescited by 3
  2. 2023PFL-GAN: When Client Heterogeneity Meets Generative Models in Personalized Federated Learningcited by 3
  3. 2024FedDistill: Global Model Distillation for Local Model De-Biasing in Non-IID Federated Learningcited by 3
  4. 2024Synthetic Data Aided Federated Learning Using Foundation Modelscited by 3
  5. 2023Personalized Privacy-Preserving Framework for Cross-Silo Federated Learningcited by 2
  6. 2023Synthetic data shuffling accelerates the convergence of federated learning under data heterogeneitycited by 2
  7. 2025FedBM: Stealing Knowledge from Pre-trained Language Models for Heterogeneous Federated Learningcited by 2
  8. 2023Data Collaboration Analysis applied to Compound Datasets and the Introduction of Projection data to Non-IID settingscited by 1
  9. 2024Federated Learning for Traffic Flow Prediction with Synthetic Data Augmentationcited by 1
  10. 2024FedCAR: Cross-client Adaptive Re-weighting for Generative Models in Federated Learningcited by 0
  11. 2025Diffusion Model-Based Data Synthesis Aided Federated Semi-Supervised Learningcited by 0
  12. 2025Federated Learning for Large-Scale Cloud Robotic Manipulation: Opportunities and Challengescited by 0
  13. 2026FedASAP: Activation Statistics-driven Structured Adaptive Pruning for Efficient Personalized Federated Learning for Lesion Segmentation on brain MRIcited by 0

Similar, not cited

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  1. 2025Personalized Federated Training of Diffusion Models with Privacy Guarantees
  2. 2024Stable Diffusion-based Data Augmentation for Federated Learning with Non-IID Data
  3. 2024Data-Free Federated Class Incremental Learning with Diffusion-Based Generative Memory
  4. 2024SiloFuse: Cross-silo Synthetic Data Generation with Latent Tabular Diffusion Models
  5. 2024DP-TLDM: Differentially Private Tabular Latent Diffusion Model
  6. 2025Data-Free Continual Learning of Server Models in Model-Heterogeneous Cloud-Device Collaboration
  7. 2026FedFG: Privacy-Preserving and Robust Federated Learning via Flow-Matching Generation
  8. 2025POPri: Private Federated Learning using Preference-Optimized Synthetic Data