mlsanityData to 14 Oct 2026

Bayesian Federated Learning: A Survey

arXiv 2304.13267 ↗First posted 26 Apr 2023cs.LG cs.AI cs.DCcited by 22

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Abstract (arXiv)

Federated learning (FL) demonstrates its advantages in integrating distributed infrastructure, communication, computing and learning in a privacy-preserving manner. However, the robustness and capabilities of existing FL methods are challenged by limited and dynamic data and conditions, complexities including heterogeneities and uncertainties, and analytical explainability. Bayesian federated learning (BFL) has emerged as a promising approach to address these issues. This survey presents a critical overview of BFL, including its basic concepts, its relations to Bayesian learning in the context of FL, and a taxonomy of BFL from both Bayesian and federated perspectives. We categorize and discuss client- and server-side and FL-based BFL methods and their pros and cons. The limitations of the existing BFL methods and the future directions of BFL research further address the intricate requirements of real-life FL applications.

Key facts. Cited by 22 papers in the map (data through 14 Oct 2026). Builds on 39 of its 82 references that are in the map. First posted on arXiv 26 Apr 2023.

Builds on

39 of 82 references.

References · 39

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2019Advances and Open Problems in Federated Learningcited by 3,046
  3. 2017What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?cited by 2,352
  4. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,538
  5. 2020Personalized Federated Learning with Moreau Envelopescited by 618
  6. 2020Federated Learning with Matched Averagingcited by 564
  7. 2021Federated Learning Based on Dynamic Regularizationcited by 562
  8. 2021Towards Personalized Federated Learningcited by 464
  9. 2019Bayesian Nonparametric Federated Learning of Neural Networkscited by 387
  10. 2017Variational Continual Learningcited by 339
  11. 2020Hands-on Bayesian Neural Networks -- a Tutorial for Deep Learning Userscited by 338
  12. 2020Federated learning with hierarchical clustering of local updates to improve training on non-IID datacited by 223
27 more
  1. 2019Client-Edge-Cloud Hierarchical Federated Learningcited by 202
  2. 2020FedBE: Making Bayesian Model Ensemble Applicable to Federated Learningcited by 176
  3. 2020FedPD: A Federated Learning Framework with Optimal Rates and Adaptivity to Non-IID Datacited by 144
  4. 2019Functional Variational Bayesian Neural Networkscited by 127
  5. 2019Federated Learning over Wireless Networks: Convergence Analysis and Resource Allocationcited by 94
  6. 2020Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithmscited by 78
  7. 2020Federated Bayesian Optimization via Thompson Samplingcited by 77
  8. 2021Personalized Federated Learning with Gaussian Processescited by 76
  9. 2020From Local SGD to Local Fixed-Point Methods for Federated Learningcited by 74
  10. 2019Variational Federated Multi-Task Learningcited by 69
  11. 2022Personalized Federated Learning via Variational Bayesian Inferencecited by 68
  12. 2019Federated Learning with Bayesian Differential Privacycited by 60
  13. 2021FedCor: Correlation-Based Active Client Selection Strategy for Heterogeneous Federated Learningcited by 43
  14. 2021A Bayesian Federated Learning Framework with Online Laplace Approximationcited by 41
  15. 2022On Privacy and Personalization in Cross-Silo Federated Learningcited by 36
  16. 2020Federated Generalized Bayesian Learning via Distributed Stein Variational Gradient Descentcited by 35
  17. 2022FedPop: A Bayesian Approach for Personalised Federated Learningcited by 35
  18. 2022SoteriaFL: A Unified Framework for Private Federated Learning with Communication Compressioncited by 34
  19. 2020Bayesian Few-Shot Classification with One-vs-Each P'olya-Gamma Augmented Gaussian Processescited by 32
  20. 2021Towards Federated Bayesian Network Structure Learning with Continuous Optimizationcited by 31
  21. 2021QLSD: Quantised Langevin stochastic dynamics for Bayesian federated learningcited by 26
  22. 2022FedNew: A Communication-Efficient and Privacy-Preserving Newton-Type Method for Federated Learningcited by 23
  23. 2023Federated Learning as Variational Inference: A Scalable Expectation Propagation Approachcited by 17
  24. 2022Self-Aware Personalized Federated Learningcited by 16
  25. 2021Federated Deep Learning with Bayesian Privacycited by 13
  26. 2020Non-IID Recommender Systems: A Review and Framework of Recommendation Paradigm Shiftingcited by 6
  27. 2023eVAE: Evolutionary Variational Autoencodercited by 6

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 3; 2024: 9; 2025: 7; 2026: 3.

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  1. 2024Federated Distillation: A Surveycited by 15
  2. 2024FedSI: Federated Subnetwork Inference for Efficient Uncertainty Quantificationcited by 4
  3. 2026Do Compact SSL Backbones Matter for Audio Deepfake Detection? A Controlled Study with RAPTORcited by 4
  4. 2024A Bayesian Framework for Clustered Federated Learningcited by 3
  5. 2025FedBEns: One-Shot Federated Learning based on Bayesian Ensemblecited by 3
  6. 2023Bayesian Personalized Federated Learning with Shared and Personalized Uncertainty Representationscited by 2
  7. 2023Personalized Federated Learning via Amortized Bayesian Meta-Learningcited by 1
  8. 2023PPFL: A Personalized Federated Learning Framework for Heterogeneous Populationcited by 1
  9. 2024FedMAP: Personalised Federated Learning for Real Large-Scale Healthcare Systemscited by 1
  10. 2024Information-Geometric Barycenters for Bayesian Federated Learningcited by 1
  11. 2025UFGraphFR: Graph Federation Recommendation System based on User Text description featurescited by 1
  12. 2025Federated Variational Inference for Bayesian Mixture Modelscited by 1
10 more
  1. 2025Personalized Bayesian Federated Learning with Wasserstein Barycenter Aggregationcited by 1
  2. 2024Bayesian Federated Inference for regression models based on non-shared multicenter data sets from heterogeneous populationscited by 0
  3. 2024Efficient Incremental Belief Updates Using Weighted Virtual Observationscited by 0
  4. 2024Robust Federated Learning for Wireless Networks: A Demonstration with Channel Estimationcited by 0
  5. 2024Compressed Bayesian Federated Learning for Reliable Passive Radio Sensing in Industrial IoTcited by 0
  6. 2025Bayesian Federated Learning for Continual Trainingcited by 0
  7. 2025Bayesian Federated Cause-of-Death Classification and Quantification Under Distribution Shiftcited by 0
  8. 2025Cost-Free Personalization via Information-Geometric Projection in Bayesian Federated Learningcited by 0
  9. 2026Bayesian Adversarial Privacycited by 0
  10. 2026TRACE: Transition-Aware Residual Control for Multi-Objective Materials Discoverycited by 0