mlsanityData to 7 Oct 2026

One-shot Federated Learning Methods: A Practical Guide

arXiv 2502.09104 ↗First posted 13 Feb 2025cs.LG cs.AIcited by 6

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Abstract (arXiv)

One-shot Federated Learning (OFL) is a distributed machine learning paradigm that constrains client-server communication to a single round, addressing privacy and communication overhead issues associated with multiple rounds of data exchange in traditional Federated Learning (FL). OFL demonstrates the practical potential for integration with future approaches that require collaborative training models, such as large language models (LLMs). However, current OFL methods face two major challenges: data heterogeneity and model heterogeneity, which result in subpar performance compared to conventional FL methods. Worse still, despite numerous studies addressing these limitations, a comprehensive summary is still lacking. To address these gaps, this paper presents a systematic analysis of the challenges faced by OFL and thoroughly reviews the current methods. We also offer an innovative categorization method and analyze the trade-offs of various techniques. Additionally, we discuss the most promising future directions and the technologies that should be integrated into the OFL field. This work aims to provide guidance and insights for future research.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 43 of its 67 references that are in the map. First posted on arXiv 13 Feb 2025.

Builds on

43 of 67 references.

References · 43

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  2. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  3. 2019Advances and Open Problems in Federated Learningcited by 3,021
  4. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  5. 2021Generalized Out-of-Distribution Detection: A Surveycited by 796
  6. 2021Model-Contrastive Federated Learningcited by 762
  7. 2017Federated Multi-Task Learningcited by 669
  8. 2021Federated Learning on Non-IID Data Silos: An Experimental Studycited by 566
  9. 2021FedBN: Federated Learning on Non-IID Features via Local Batch Normalizationcited by 565
  10. 2020Federated Learning with Matched Averagingcited by 564
  11. 2024Mixture-of-Agents Enhances Large Language Model Capabilitiescited by 420
  12. 2019Bayesian Nonparametric Federated Learning of Neural Networkscited by 385
31 more
  1. 2020HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clientscited by 334
  2. 2019One-Shot Federated Learningcited by 124
  3. 2024OpenFedLLM: Training Large Language Models on Decentralized Private Data via Federated Learningcited by 124
  4. 2020Distilled One-Shot Federated Learningcited by 108
  5. 2021DENSE: Data-Free One-Shot Federated Learningcited by 100
  6. 2021Heterogeneity for the Win: One-Shot Federated Clusteringcited by 87
  7. 2020Practical One-Shot Federated Learning for Cross-Silo Settingcited by 76
  8. 2020XOR Mixup: Privacy-Preserving Data Augmentation for One-Shot Federated Learningcited by 58
  9. 2022Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learningcited by 52
  10. 2022Federated Learning via Decentralized Dataset Distillation in Resource-Constrained Edge Environmentscited by 49
  11. 2022Preserving Privacy in Federated Learning with Ensemble Cross-Domain Knowledge Distillationcited by 45
  12. 2023Exploring One-shot Semi-supervised Federated Learning with A Pre-trained Diffusion Modelcited by 33
  13. 2022Knowledge Distillation for Federated Learning: a Practical Guidecited by 31
  14. 2024A Survey on Efficient Federated Learning Methods for Foundation Model Trainingcited by 29
  15. 2024Enhancing One-Shot Federated Learning Through Data and Ensemble Co-Boostingcited by 27
  16. 2023One-Shot Federated Conformal Predictioncited by 26
  17. 2023FedLPA: One-shot Federated Learning with Layer-Wise Posterior Aggregationcited by 22
  18. 2022One-shot Federated Learning without Server-side Trainingcited by 20
  19. 2024FedDEO: Description-Enhanced One-Shot Federated Learning with Diffusion Modelscited by 17
  20. 2024FedBiP: Heterogeneous One-Shot Federated Learning with Personalized Latent Diffusion Modelscited by 16
  21. 2024Parametric Feature Transfer: One-shot Federated Learning with Foundation Modelscited by 13
  22. 2024Revisiting Ensembling in One-Shot Federated Learningcited by 13
  23. 2024One-shot Federated Learning via Synthetic Distiller-Distillate Communicationcited by 12
  24. 2024FuseFL: One-Shot Federated Learning through the Lens of Causality with Progressive Model Fusioncited by 11
  25. 2023Calibrated One Round Federated Learning with Bayesian Inference in the Predictive Spacecited by 10
  26. 2024Effective Heterogeneous Federated Learning via Efficient Hypernetwork-based Weight Generationcited by 9
  27. 2022How Does a Deep Learning Model Architecture Impact Its Privacy? A Comprehensive Study of Privacy Attacks on CNNs and Transformerscited by 8
  28. 2024One-Shot Sequential Federated Learning for Non-IID Data by Enhancing Local Model Diversitycited by 8
  29. 2024FusionLLM: A Decentralized LLM Training System on Geo-distributed GPUs with Adaptive Compressioncited by 7
  30. 2025Mediator: Memory-efficient LLM Merging with Less Parameter Conflicts and Uncertainty Based Routingcited by 7
  31. 2024OFL-W3: A One-shot Federated Learning System on Web 3.0cited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 4.

6 papers.

  1. 2025CATCH: A Modular Cross-domain Adaptive Template with Hookcited by 6
  2. 2026FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Predictioncited by 3
  3. 2025Towards One-shot Federated Learning: Advances, Challenges, and Future Directionscited by 2
  4. 2026Single-Round Clustered Federated Learning via Data Collaboration Analysis for Non-IID Datacited by 1
  5. 2026Distribution Alignment for One-Shot Federated Learning via Optimal Transportcited by 1
  6. 2026MOSAIC: Orchestrating Collaborative Knowledge Tracing with Hierarchical Semantic Alignmentcited by 0