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Practical One-Shot Federated Learning for Cross-Silo Setting

arXiv 2010.01017 ↗First posted 2 Oct 2020cs.LG stat.MLcited by 76

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Abstract (arXiv)

Federated learning enables multiple parties to collaboratively learn a model without exchanging their data. While most existing federated learning algorithms need many rounds to converge, one-shot federated learning (i.e., federated learning with a single communication round) is a promising approach to make federated learning applicable in cross-silo setting in practice. However, existing one-shot algorithms only support specific models and do not provide any privacy guarantees, which significantly limit the applications in practice. In this paper, we propose a practical one-shot federated learning algorithm named FedKT. By utilizing the knowledge transfer technique, FedKT can be applied to any classification models and can flexibly achieve differential privacy guarantees. Our experiments on various tasks show that FedKT can significantly outperform the other state-of-the-art federated learning algorithms with a single communication round.

Key facts. Cited by 76 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Oct 2020.

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Citing papers per year: 2021: 4; 2022: 8; 2023: 18; 2024: 26; 2025: 13; 2026: 7.

76 papers.

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  1. 2021Federated Learning on Non-IID Data Silos: An Experimental Studycited by 566
  2. 2022Federated Learning with Label Distribution Skew via Logits Calibrationcited by 108
  3. 2021DENSE: Data-Free One-Shot Federated Learningcited by 100
  4. 2023FedGH: Heterogeneous Federated Learning with Generalized Global Headercited by 56
  5. 2022Federated Learning via Decentralized Dataset Distillation in Resource-Constrained Edge Environmentscited by 49
  6. 2021Towards Model Agnostic Federated Learning Using Knowledge Distillationcited by 31
  7. 2022FedDUAP: Federated Learning with Dynamic Update and Adaptive Pruning Using Shared Data on the Servercited by 29
  8. 2024Enhancing One-Shot Federated Learning Through Data and Ensemble Co-Boostingcited by 27
  9. 2023One-Shot Federated Conformal Predictioncited by 26
  10. 2022A Survey on Federated Recommendation Systemscited by 25
  11. 2021Personalized Federated Learning for Heterogeneous Clients with Clustered Knowledge Transfercited by 23
  12. 2023FedLPA: One-shot Federated Learning with Layer-Wise Posterior Aggregationcited by 22
38 more
  1. 2024Federated Model Heterogeneous Matryoshka Representation Learningcited by 21
  2. 2022One-shot Federated Learning without Server-side Trainingcited by 20
  3. 2024FedBiP: Heterogeneous One-Shot Federated Learning with Personalized Latent Diffusion Modelscited by 16
  4. 2024FedFisher: Leveraging Fisher Information for One-Shot Federated Learningcited by 15
  5. 2024Federated Distillation: A Surveycited by 14
  6. 2024Parametric Feature Transfer: One-shot Federated Learning with Foundation Modelscited by 13
  7. 2024Revisiting Ensembling in One-Shot Federated Learningcited by 13
  8. 2024Task Arithmetic Through The Lens Of One-Shot Federated Learningcited by 12
  9. 2024One-shot Federated Learning via Synthetic Distiller-Distillate Communicationcited by 12
  10. 2024FuseFL: One-Shot Federated Learning through the Lens of Causality with Progressive Model Fusioncited by 11
  11. 2023FedSSA: Semantic Similarity-based Aggregation for Efficient Model-Heterogeneous Personalized Federated Learningcited by 10
  12. 2023Calibrated One Round Federated Learning with Bayesian Inference in the Predictive Spacecited by 10
  13. 2023pFedES: Model Heterogeneous Personalized Federated Learning with Feature Extractor Sharingcited by 9
  14. 2024A Comprehensive Survey of Federated Transfer Learning: Challenges, Methods and Applicationscited by 8
  15. 2024One-Shot Sequential Federated Learning for Non-IID Data by Enhancing Local Model Diversitycited by 8
  16. 2024Knowledge Distillation in Federated Learning: a Survey on Long Lasting Challenges and New Solutionscited by 8
  17. 2023One-Shot Federated Learning for LEO Constellations that Reduces Convergence Time from Days to 90 Minutescited by 7
  18. 2024MAP: Model Aggregation and Personalization in Federated Learning with Incomplete Classescited by 7
  19. 2025One-Shot Federated Learning with Classifier-Free Diffusion Modelscited by 7
  20. 2023Federated Learning via Input-Output Collaborative Distillationcited by 6
  21. 2024Efficient Federated Learning Using Dynamic Update and Adaptive Pruning with Momentum on Shared Server Datacited by 6
  22. 2025One-shot Federated Learning Methods: A Practical Guidecited by 6
  23. 2025Capture Global Feature Statistics for One-Shot Federated Learningcited by 6
  24. 2023Data-Aware Gradient Compression for FL in Communication-Constrained Mobile Computingcited by 5
  25. 2023One-Shot Heterogeneous Federated Learning with Local Model-Guided Diffusion Modelscited by 5
  26. 2023FedAL: Black-Box Federated Knowledge Distillation Enabled by Adversarial Learningcited by 5
  27. 2022FedSPLIT: One-Shot Federated Recommendation System Based on Non-negative Joint Matrix Factorization and Knowledge Distillationcited by 4
  28. 2023Data-Free Diversity-Based Ensemble Selection For One-Shot Federated Learning in Machine Learning Model Marketcited by 4
  29. 2023FedPDD: A Privacy-preserving Double Distillation Framework for Cross-silo Federated Recommendationcited by 4
  30. 2024DFML: Decentralized Federated Mutual Learningcited by 4
  31. 2024Exploring the Practicality of Federated Learning: A Survey Towards the Communication Perspectivecited by 4
  32. 2023Adaptive Parameterization of Deep Learning Models for Federated Learningcited by 3
  33. 2023Towards Open Federated Learning Platforms: Survey and Vision from Technical and Legal Perspectivescited by 3
  34. 2024A Federated Learning Benchmark on Tabular Data: Comparing Tree-Based Models and Neural Networkscited by 3
  35. 2024A Unified Solution to Diverse Heterogeneities in One-shot Federated Learningcited by 3
  36. 2025Research in Collaborative Learning Does Not Serve Cross-Silo Federated Learning in Practicecited by 3
  37. 2024DFDG: Data-Free Dual-Generator Adversarial Distillation for One-Shot Federated Learningcited by 2
  38. 2024FedPeWS: Personalized Warmup via Subnetworks for Enhanced Heterogeneous Federated Learningcited by 2

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