mlsanityData to 7 Oct 2026

FedBEns: One-Shot Federated Learning based on Bayesian Ensemble

arXiv 2503.15367 ↗First posted 19 Mar 2025cs.LGcited by 3

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Abstract (arXiv)

One-Shot Federated Learning (FL) is a recent paradigm that enables multiple clients to cooperatively learn a global model in a single round of communication with a central server. In this paper, we analyze the One-Shot FL problem through the lens of Bayesian inference and propose FedBEns, an algorithm that leverages the inherent multimodality of local loss functions to find better global models. Our algorithm leverages a mixture of Laplace approximations for the clients' local posteriors, which the server then aggregates to infer the global model. We conduct extensive experiments on various datasets, demonstrating that the proposed method outperforms competing baselines that typically rely on unimodal approximations of the local losses.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 36 of its 47 references that are in the map. First posted on arXiv 19 Mar 2025.

Builds on

36 of 47 references.

References · 36

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24 more
  1. 2019Deep Ensembles: A Loss Landscape Perspectivecited by 438
  2. 2022Dataless Knowledge Fusion by Merging Weights of Language Modelscited by 384
  3. 2021Laplace Redux -- Effortless Bayesian Deep Learningcited by 359
  4. 2021What Are Bayesian Neural Network Posteriors Really Like?cited by 307
  5. 2019The Secret Revealer: Generative Model-Inversion Attacks Against Deep Neural Networkscited by 241
  6. 2020Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networkscited by 227
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  11. 2019One-Shot Federated Learningcited by 124
  12. 2022REPAIR: REnormalizing Permuted Activations for Interpolation Repaircited by 119
  13. 2020Distilled One-Shot Federated Learningcited by 108
  14. 2021DENSE: Data-Free One-Shot Federated Learningcited by 100
  15. 2020Bayesian Deep Learning via Subnetwork Inferencecited by 85
  16. 2022Heterogeneous Ensemble Knowledge Transfer for Training Large Models in Federated Learningcited by 81
  17. 2020Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithmscited by 78
  18. 2022Preserving Privacy in Federated Learning with Ensemble Cross-Domain Knowledge Distillationcited by 45
  19. 2021Mixtures of Laplace Approximations for Improved Post-Hoc Uncertainty in Deep Learningcited by 25
  20. 2023Bayesian Federated Learning: A Surveycited by 22
  21. 2023FedLPA: One-shot Federated Learning with Layer-Wise Posterior Aggregationcited by 22
  22. 2023Federated Learning as Variational Inference: A Scalable Expectation Propagation Approachcited by 17
  23. 2024Revisiting Ensembling in One-Shot Federated Learningcited by 13
  24. 2023Calibrated One Round Federated Learning with Bayesian Inference in the Predictive Spacecited by 10

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2025TOFA: Training-Free One-Shot Federated Adaptation for Vision-Language Modelscited by 1
  2. 2026Distribution Alignment for One-Shot Federated Learning via Optimal Transportcited by 1
  3. 2026CRIP: Channel Level Representation Injection for Personalized One-Shot Federated Learningcited by 0