# Bayesian Federated Learning: A Survey

arXiv: https://arxiv.org/abs/2304.13267 · HTML: https://mlsanity.com/paper/2304.13267/

## 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 (most cited first, 30 of 39)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Personalized Federated Learning with Moreau Envelopes](https://mlsanity.com/paper/2006.08848/) (2020)
- [Federated Learning with Matched Averaging](https://mlsanity.com/paper/2002.06440/) (2020)
- [Federated Learning Based on Dynamic Regularization](https://mlsanity.com/paper/2111.04263/) (2021)
- [Towards Personalized Federated Learning](https://mlsanity.com/paper/2103.00710/) (2021)
- [Bayesian Nonparametric Federated Learning of Neural Networks](https://mlsanity.com/paper/1905.12022/) (2019)
- [Variational Continual Learning](https://mlsanity.com/paper/1710.10628/) (2017)
- [Hands-on Bayesian Neural Networks -- a Tutorial for Deep Learning Users](https://mlsanity.com/paper/2007.06823/) (2020)
- [Federated learning with hierarchical clustering of local updates to improve training on non-IID data](https://mlsanity.com/paper/2004.11791/) (2020)
- [Client-Edge-Cloud Hierarchical Federated Learning](https://mlsanity.com/paper/1905.06641/) (2019)
- [FedBE: Making Bayesian Model Ensemble Applicable to Federated Learning](https://mlsanity.com/paper/2009.01974/) (2020)
- [FedPD: A Federated Learning Framework with Optimal Rates and Adaptivity to Non-IID Data](https://mlsanity.com/paper/2005.11418/) (2020)
- [Functional Variational Bayesian Neural Networks](https://mlsanity.com/paper/1903.05779/) (2019)
- [Federated Learning over Wireless Networks: Convergence Analysis and Resource Allocation](https://mlsanity.com/paper/1910.13067/) (2019)
- [Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms](https://mlsanity.com/paper/2010.05273/) (2020)
- [Federated Bayesian Optimization via Thompson Sampling](https://mlsanity.com/paper/2010.10154/) (2020)
- [Personalized Federated Learning with Gaussian Processes](https://mlsanity.com/paper/2106.15482/) (2021)
- [From Local SGD to Local Fixed-Point Methods for Federated Learning](https://mlsanity.com/paper/2004.01442/) (2020)
- [Variational Federated Multi-Task Learning](https://mlsanity.com/paper/1906.06268/) (2019)
- [Personalized Federated Learning via Variational Bayesian Inference](https://mlsanity.com/paper/2206.07977/) (2022)
- [Federated Learning with Bayesian Differential Privacy](https://mlsanity.com/paper/1911.10071/) (2019)
- [FedCor: Correlation-Based Active Client Selection Strategy for Heterogeneous Federated Learning](https://mlsanity.com/paper/2103.13822/) (2021)
- [A Bayesian Federated Learning Framework with Online Laplace Approximation](https://mlsanity.com/paper/2102.01936/) (2021)
- [On Privacy and Personalization in Cross-Silo Federated Learning](https://mlsanity.com/paper/2206.07902/) (2022)
- [Federated Generalized Bayesian Learning via Distributed Stein Variational Gradient Descent](https://mlsanity.com/paper/2009.06419/) (2020)
- [FedPop: A Bayesian Approach for Personalised Federated Learning](https://mlsanity.com/paper/2206.03611/) (2022)
- [SoteriaFL: A Unified Framework for Private Federated Learning with Communication Compression](https://mlsanity.com/paper/2206.09888/) (2022)

## Built on by (most cited first, 22 of 22)

- [Federated Distillation: A Survey](https://mlsanity.com/paper/2404.08564/) (2024)
- [FedSI: Federated Subnetwork Inference for Efficient Uncertainty Quantification](https://mlsanity.com/paper/2404.15657/) (2024)
- [Do Compact SSL Backbones Matter for Audio Deepfake Detection? A Controlled Study with RAPTOR](https://mlsanity.com/paper/2603.06164/) (2026)
- [A Bayesian Framework for Clustered Federated Learning](https://mlsanity.com/paper/2410.15473/) (2024)
- [FedBEns: One-Shot Federated Learning based on Bayesian Ensemble](https://mlsanity.com/paper/2503.15367/) (2025)
- [Bayesian Personalized Federated Learning with Shared and Personalized Uncertainty Representations](https://mlsanity.com/paper/2309.15499/) (2023)
- [Personalized Federated Learning via Amortized Bayesian Meta-Learning](https://mlsanity.com/paper/2307.02222/) (2023)
- [PPFL: A Personalized Federated Learning Framework for Heterogeneous Population](https://mlsanity.com/paper/2310.14337/) (2023)
- [FedMAP: Personalised Federated Learning for Real Large-Scale Healthcare Systems](https://mlsanity.com/paper/2405.19000/) (2024)
- [Information-Geometric Barycenters for Bayesian Federated Learning](https://mlsanity.com/paper/2412.11646/) (2024)
- [UFGraphFR: Graph Federation Recommendation System based on User Text description features](https://mlsanity.com/paper/2501.08044/) (2025)
- [Federated Variational Inference for Bayesian Mixture Models](https://mlsanity.com/paper/2502.12684/) (2025)
- [Personalized Bayesian Federated Learning with Wasserstein Barycenter Aggregation](https://mlsanity.com/paper/2505.14161/) (2025)
- [Bayesian Federated Inference for regression models based on non-shared multicenter data sets from heterogeneous populations](https://mlsanity.com/paper/2402.02898/) (2024)
- [Efficient Incremental Belief Updates Using Weighted Virtual Observations](https://mlsanity.com/paper/2402.06940/) (2024)
- [Robust Federated Learning for Wireless Networks: A Demonstration with Channel Estimation](https://mlsanity.com/paper/2404.03088/) (2024)
- [Compressed Bayesian Federated Learning for Reliable Passive Radio Sensing in Industrial IoT](https://mlsanity.com/paper/2405.05855/) (2024)
- [Bayesian Federated Learning for Continual Training](https://mlsanity.com/paper/2504.15328/) (2025)
- [Bayesian Federated Cause-of-Death Classification and Quantification Under Distribution Shift](https://mlsanity.com/paper/2505.02257/) (2025)
- [Cost-Free Personalization via Information-Geometric Projection in Bayesian Federated Learning](https://mlsanity.com/paper/2509.10132/) (2025)
- [Bayesian Adversarial Privacy](https://mlsanity.com/paper/2603.04199/) (2026)
- [TRACE: Transition-Aware Residual Control for Multi-Objective Materials Discovery](https://mlsanity.com/paper/2608.23631/) (2026)

## 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.