mlsanityData to 7 Oct 2026

Federated Learning via Synthetic Data

arXiv 2008.04489 ↗First posted 11 Aug 2020cs.LG stat.MLcited by 53

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Abstract (arXiv)

Federated learning allows for the training of a model using data on multiple clients without the clients transmitting that raw data. However the standard method is to transmit model parameters (or updates), which for modern neural networks can be on the scale of millions of parameters, inflicting significant computational costs on the clients. We propose a method for federated learning where instead of transmitting a gradient update back to the server, we instead transmit a small amount of synthetic `data'. We describe the procedure and show some experimental results suggesting this procedure has potential, providing more than an order of magnitude reduction in communication costs with minimal model degradation.

Key facts. Cited by 53 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Aug 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 12; 2023: 12; 2024: 18; 2025: 5; 2026: 3.

53 papers.

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  1. 2022Dataset Distillation by Matching Training Trajectoriescited by 437
  2. 2021No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Datacited by 241
  3. 2022Dataset Distillation using Neural Feature Regressioncited by 177
  4. 2022Minimizing the Accumulated Trajectory Error to Improve Dataset Distillationcited by 142
  5. 2023Dataset Distillation: A Comprehensive Reviewcited by 126
  6. 2023Improved Distribution Matching for Dataset Condensationcited by 124
  7. 2023A Comprehensive Survey of Dataset Distillationcited by 114
  8. 2023Data Distillation: A Surveycited by 86
  9. 2021Towards Fair Federated Learning with Zero-Shot Data Augmentationcited by 64
  10. 2022Accelerating Dataset Distillation via Model Augmentationcited by 62
  11. 2023FedFed: Feature Distillation against Data Heterogeneity in Federated Learningcited by 54
  12. 2022Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learningcited by 52
38 more
  1. 2022Federated Learning via Decentralized Dataset Distillation in Resource-Constrained Edge Environmentscited by 49
  2. 2023Sequential Subset Matching for Dataset Distillationcited by 40
  3. 2023Dataset Distillation via the Wasserstein Metriccited by 31
  4. 2022Architecture Agnostic Federated Learning for Neural Networkscited by 30
  5. 2022FedSynth: Gradient Compression via Synthetic Data in Federated Learningcited by 30
  6. 2022Acceleration of Federated Learning with Alleviated Forgetting in Local Trainingcited by 29
  7. 2022Rethinking Data Heterogeneity in Federated Learning: Introducing a New Notion and Standard Benchmarkscited by 24
  8. 2024Federated Large Language Models: Current Progress and Future Directionscited by 22
  9. 2021A Unified Approach to Coreset Learningcited by 18
  10. 2022Federated Self-supervised Learning for Heterogeneous Clientscited by 16
  11. 2023Visual Prompt Based Personalized Federated Learningcited by 16
  12. 2024From Challenges and Pitfalls to Recommendations and Opportunities: Implementing Federated Learning in Healthcarecited by 16
  13. 2023Dataset Condensation via Generative Modelcited by 15
  14. 2024FedImpro: Measuring and Improving Client Update in Federated Learningcited by 15
  15. 2024Not All Samples Should Be Utilized Equally: Towards Understanding and Improving Dataset Distillationcited by 14
  16. 2024FuseFL: One-Shot Federated Learning through the Lens of Causality with Progressive Model Fusioncited by 11
  17. 2024Stable Diffusion-based Data Augmentation for Federated Learning with Non-IID Datacited by 10
  18. 2022FedSyn: Synthetic Data Generation using Federated Learningcited by 7
  19. 2023Federated Learning on Virtual Heterogeneous Data with Local-global Distillationcited by 6
  20. 2024Dataset Condensation with Latent Quantile Matchingcited by 6
  21. 2023Communication-efficient Federated Learning with Single-Step Synthetic Features Compressor for Faster Convergencecited by 5
  22. 2024Label-Augmented Dataset Distillationcited by 5
  23. 2025On Learning Representations for Tabular Data Distillationcited by 4
  24. 2025E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizingcited by 3
  25. 2023Synthetic data shuffling accelerates the convergence of federated learning under data heterogeneitycited by 2
  26. 2024Tackling Data Heterogeneity in Federated Time Series Forecastingcited by 2
  27. 2024Exemplar-condensed Federated Class-incremental Learningcited by 2
  28. 2024FedSR: A Semi-Decentralized Federated Learning Algorithm for Non-IIDness in IoT Systemcited by 1
  29. 2024One-Shot Federated Learning with Bayesian Pseudocoresetscited by 1
  30. 2024One-Shot Collaborative Data Distillationcited by 1
  31. 2025FedLoRA-Optimizer: Federated LoRA Fine-Tuning with Global and Local Optimization in Heterogeneous Data Scenarioscited by 1
  32. 2024CoDream: Exchanging dreams instead of models for federated aggregation with heterogeneous modelscited by 0
  33. 2024Approximate Gradient Coding for Privacy-Flexible Federated Learning with Non-IID Datacited by 0
  34. 2024Federated Learning Optimization: A Comparative Study of Data and Model Exchange Strategies in Dynamic Networkscited by 0
  35. 2024UDD: Dataset Distillation via Mining Underutilized Regionscited by 0
  36. 2024Using Synthetic Data to Mitigate Unfairness and Preserve Privacy in Collaborative Machine Learningcited by 0
  37. 2025FedWCM: Unleashing the Potential of Momentum-based Federated Learning in Long-Tailed Scenarioscited by 0
  38. 2025Secure and Explainable Fraud Detection in Finance via Hierarchical Multi-source Dataset Distillationcited by 0

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