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Practical One-Shot Federated Learning for Cross-Silo Setting

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arxiv 2010.01017 v2 pith:LXGAGEEP submitted 2020-10-02 cs.LG stat.ML

Qinbin Li , Bingsheng He , Dawn Song This is my paper · ORCID
classification cs.LGstat.ML
keywords federatedlearningone-shotalgorithmsfedktcommunicationcross-siloexisting
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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.

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Cited by 5 Pith papers

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  1. Robust Federated Learning Under Real-World Client Churn

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    FeLiX reduces wall-clock time-to-target accuracy in federated learning by up to 2.37x using lightweight availability tiers, fresh-utility client selection, and informativeness-aware aggregation without requiring oracu...

  2. A New One-Shot Federated Learning Framework for Medical Imaging Classification with Feature-Guided Rectified Flow and Knowledge Distillation

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    Feature-level rectified flow generation plus dual-layer knowledge distillation yields a one-shot federated learning method that beats several baselines on three non-IID medical imaging datasets.

  3. One-shot Federated Learning via Synthetic Distiller-Distillate Communication

    cs.LG 2024-12 conditional novelty 6.0 of 10

    FedSD2C beats prior one-shot federated learning baselines on ImageNette, Tiny-ImageNet, and OpenImage by sending compact latent codes of selected, Fourier-perturbed images instead of local models.

  4. Task Arithmetic Through The Lens Of One-Shot Federated Learning

    cs.LG 2024-11 conditional novelty 5.0 of 10

    Task arithmetic is exactly one-shot FedAvg with outer step size beta = lambda T, and FedNova, FedGMA, Median, and CCLIP can often improve merged model performance.

  5. Towards One-shot Federated Learning: Advances, Challenges, and Future Directions

    cs.LG 2025-05 conditional novelty 1.0 of 10

    A literature survey of one-shot federated learning that organizes methods, datasets, and code, but reports no new experimental or theoretical results.

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