mlsanityData to 7 Oct 2026

Enhancing One-Shot Federated Learning Through Data and Ensemble Co-Boosting

arXiv 2402.15070 ↗First posted 23 Feb 2024cs.LGcited by 27

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Abstract (arXiv)

One-shot Federated Learning (OFL) has become a promising learning paradigm, enabling the training of a global server model via a single communication round. In OFL, the server model is aggregated by distilling knowledge from all client models (the ensemble), which are also responsible for synthesizing samples for distillation. In this regard, advanced works show that the performance of the server model is intrinsically related to the quality of the synthesized data and the ensemble model. To promote OFL, we introduce a novel framework, Co-Boosting, in which synthesized data and the ensemble model mutually enhance each other progressively. Specifically, Co-Boosting leverages the current ensemble model to synthesize higher-quality samples in an adversarial manner. These hard samples are then employed to promote the quality of the ensemble model by adjusting the ensembling weights for each client model. Consequently, Co-Boosting periodically achieves high-quality data and ensemble models. Extensive experiments demonstrate that Co-Boosting can substantially outperform existing baselines under various settings. Moreover, Co-Boosting eliminates the need for adjustments to the client's local training, requires no additional data or model transmission, and allows client models to have heterogeneous architectures.

Key facts. Cited by 27 papers in the map (data through 7 Oct 2026). Builds on 44 of its 63 references that are in the map. First posted on arXiv 23 Feb 2024.

Builds on

44 of 63 references.

References · 44

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 7; 2025: 11; 2026: 9.

27 papers.

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  1. 2024FedBiP: Heterogeneous One-Shot Federated Learning with Personalized Latent Diffusion Modelscited by 16
  2. 2024Revisiting Ensembling in One-Shot Federated Learningcited by 13
  3. 2024One-shot Federated Learning via Synthetic Distiller-Distillate Communicationcited by 12
  4. 2024FuseFL: One-Shot Federated Learning through the Lens of Causality with Progressive Model Fusioncited by 11
  5. 2025One-shot Federated Learning Methods: A Practical Guidecited by 6
  6. 2025Capture Global Feature Statistics for One-Shot Federated Learningcited by 6
  7. 2024A Unified Solution to Diverse Heterogeneities in One-shot Federated Learningcited by 3
  8. 2024Personalized One-shot Federated Graph Learning for Heterogeneous Clientscited by 3
  9. 2024Tackling Data Heterogeneity in Federated Time Series Forecastingcited by 2
  10. 2025Towards One-shot Federated Learning: Advances, Challenges, and Future Directionscited by 2
  11. 2025Robust Federated Inferencecited by 2
  12. 2026The Gaussian-Head OFL Family: One-Shot Federated Learning from Client Global Statisticscited by 2
15 more
  1. 2025FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devicescited by 1
  2. 2025Enhancing Federated Class-Incremental Learning via Spatial-Temporal Statistics Aggregationcited by 1
  3. 2025A New One-Shot Federated Learning Framework for Medical Imaging Classification with Feature-Guided Rectified Flow and Knowledge Distillationcited by 1
  4. 2025Hypernetworks for Model-Heterogeneous Personalized Federated Learningcited by 1
  5. 2025TOFA: Training-Free One-Shot Federated Adaptation for Vision-Language Modelscited by 1
  6. 2026Distribution Alignment for One-Shot Federated Learning via Optimal Transportcited by 1
  7. 2025Defending against Backdoor Attacks via Module Switchingcited by 0
  8. 2025Single-Round Scalable Analytic Federated Learningcited by 0
  9. 2026FedBiCross: Personalized One-Shot Federated Learning on Medical Imagescited by 0
  10. 2026Feature-Aware One-Shot Federated Learning via Hierarchical Token Sequencescited by 0
  11. 2026Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTscited by 0
  12. 2026Collaborative Synthetic Data Generation for Knowledge Transfer in Federated Learningcited by 0
  13. 2026FedOPAL: One-Shot Federated Learning via Analytic Visual Prompt Tuningcited by 0
  14. 2026CRIP: Channel Level Representation Injection for Personalized One-Shot Federated Learningcited by 0
  15. 2026FedCC: Towards Addressing Label Distribution Skews in Distillation-Based Federated Learningcited by 0