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One-Shot Federated Learning

arXiv 1902.11175 ↗First posted 28 Feb 2019cs.LG stat.MLcited by 124

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Abstract (arXiv)

We present one-shot federated learning, where a central server learns a global model over a network of federated devices in a single round of communication. Our approach - drawing on ensemble learning and knowledge aggregation - achieves an average relative gain of 51.5% in AUC over local baselines and comes within 90.1% of the (unattainable) global ideal. We discuss these methods and identify several promising directions of future work.

Key facts. Cited by 124 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Feb 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 22; 2022: 20; 2023: 20; 2024: 30; 2025: 19; 2026: 13.

124 papers.

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  1. 2021Federated Learning on Non-IID Data Silos: An Experimental Studycited by 566
  2. 2021Data-Free Knowledge Distillation for Heterogeneous Federated Learningcited by 380
  3. 2021Federated Learning for Open Bankingcited by 138
  4. 2021Parameterized Knowledge Transfer for Personalized Federated Learningcited by 108
  5. 2021DENSE: Data-Free One-Shot Federated Learningcited by 100
  6. 2025A Comprehensive Survey of Mixture-of-Experts: Algorithms, Theory, and Applicationscited by 91
  7. 2023Deep Model Fusion: A Surveycited by 89
  8. 2021Federated Learning: Opportunities and Challengescited by 83
  9. 2022Personalized Federated Learning via Variational Bayesian Inferencecited by 68
  10. 2021FedAUX: Leveraging Unlabeled Auxiliary Data in Federated Learningcited by 67
  11. 2022FedDM: Iterative Distribution Matching for Communication-Efficient Federated Learningcited by 65
  12. 2022Federated Learning via Decentralized Dataset Distillation in Resource-Constrained Edge Environmentscited by 49
38 more
  1. 2021FedSoft: Soft Clustered Federated Learning with Proximal Local Updatingcited by 46
  2. 2021Federated Learning Meets Natural Language Processing: A Surveycited by 42
  3. 2021Communication Efficiency in Federated Learning: Achievements and Challengescited by 40
  4. 2021Improving Fairness via Federated Learningcited by 39
  5. 2021Scaling Federated Learning for Fine-tuning of Large Language Modelscited by 36
  6. 2023Cross-Silo Prototypical Calibration for Federated Learning with Non-IID Datacited by 35
  7. 2021FedZKT: Zero-Shot Knowledge Transfer towards Resource-Constrained Federated Learning with Heterogeneous On-Device Modelscited by 31
  8. 2021SemiFed: Semi-supervised Federated Learning with Consistency and Pseudo-Labelingcited by 29
  9. 2024Enhancing One-Shot Federated Learning Through Data and Ensemble Co-Boostingcited by 27
  10. 2023One-Shot Federated Conformal Predictioncited by 26
  11. 2022A Survey on Federated Recommendation Systemscited by 25
  12. 2023Federated Generative Learning with Foundation Modelscited by 23
  13. 2023FedLPA: One-shot Federated Learning with Layer-Wise Posterior Aggregationcited by 22
  14. 2022One-shot Federated Learning without Server-side Trainingcited by 20
  15. 2022Practical Vertical Federated Learning with Unsupervised Representation Learningcited by 19
  16. 2021FedDQ: Communication-Efficient Federated Learning with Descending Quantizationcited by 18
  17. 2021Evaluating Federated Learning for Intrusion Detection in Internet of Things: Review and Challengescited by 16
  18. 2024From Challenges and Pitfalls to Recommendations and Opportunities: Implementing Federated Learning in Healthcarecited by 16
  19. 2024FedBiP: Heterogeneous One-Shot Federated Learning with Personalized Latent Diffusion Modelscited by 16
  20. 2022Non-IID Quantum Federated Learning with One-shot Communication Complexitycited by 15
  21. 2022Federated Few-Shot Learning for Mobile NLPcited by 15
  22. 2024FedFisher: Leveraging Fisher Information for One-Shot Federated Learningcited by 15
  23. 2022PerFED-GAN: Personalized Federated Learning via Generative Adversarial Networkscited by 14
  24. 2024Federated Distillation: A Surveycited by 14
  25. 2024Parametric Feature Transfer: One-shot Federated Learning with Foundation Modelscited by 13
  26. 2024Revisiting Ensembling in One-Shot Federated Learningcited by 13
  27. 2021FLIX: A Simple and Communication-Efficient Alternative to Local Methods in Federated Learningcited by 12
  28. 2024Task Arithmetic Through The Lens Of One-Shot Federated Learningcited by 12
  29. 2024One-shot Federated Learning via Synthetic Distiller-Distillate Communicationcited by 12
  30. 2021Federated Few-Shot Learning with Adversarial Learningcited by 11
  31. 2024FuseFL: One-Shot Federated Learning through the Lens of Causality with Progressive Model Fusioncited by 11
  32. 2021What Can Knowledge Bring to Machine Learning? -- A Survey of Low-shot Learning for Structured Datacited by 10
  33. 2021FedMe: Federated Learning via Model Exchangecited by 10
  34. 2023Patchwork Learning: A Paradigm Towards Integrative Analysis across Diverse Biomedical Data Sourcescited by 10
  35. 2024One-Shot Sequential Federated Learning for Non-IID Data by Enhancing Local Model Diversitycited by 8
  36. 2024Knowledge Distillation in Federated Learning: a Survey on Long Lasting Challenges and New Solutionscited by 8
  37. 2023One-Shot Federated Learning for LEO Constellations that Reduces Convergence Time from Days to 90 Minutescited by 7
  38. 2024Navigating Heterogeneity and Privacy in One-Shot Federated Learning with Diffusion Modelscited by 7

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