# Federated Generalized Bayesian Learning via Distributed Stein Variational Gradient Descent

arXiv: https://arxiv.org/abs/2009.06419 · HTML: https://mlsanity.com/paper/2009.06419/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2020.

## Built on by (most cited first, 30 of 35)

- [Distributed Learning in Wireless Networks: Recent Progress and Future Challenges](https://mlsanity.com/paper/2104.02151/) (2021)
- [Personalized Federated Learning with Gaussian Processes](https://mlsanity.com/paper/2106.15482/) (2021)
- [GNNDelete: A General Strategy for Unlearning in Graph Neural Networks](https://mlsanity.com/paper/2302.13406/) (2023)
- [Federated Unlearning: A Survey on Methods, Design Guidelines, and Evaluation Metrics](https://mlsanity.com/paper/2401.05146/) (2024)
- [QLSD: Quantised Langevin stochastic dynamics for Bayesian federated learning](https://mlsanity.com/paper/2106.00797/) (2021)
- [Bayesian Federated Learning: A Survey](https://mlsanity.com/paper/2304.13267/) (2023)
- [A Convergence Theory for SVGD in the Population Limit under Talagrand's Inequality T1](https://mlsanity.com/paper/2106.03076/) (2021)
- [Federated Learning as Variational Inference: A Scalable Expectation Propagation Approach](https://mlsanity.com/paper/2302.04228/) (2023)
- [Forget-SVGD: Particle-Based Bayesian Federated Unlearning](https://mlsanity.com/paper/2111.12056/) (2021)
- [Convergence of Stein Variational Gradient Descent under a Weaker Smoothness Condition](https://mlsanity.com/paper/2206.00508/) (2022)
- [A Survey on Machine Unlearning: Techniques and New Emerged Privacy Risks](https://mlsanity.com/paper/2406.06186/) (2024)
- [Federated Learning with Uncertainty via Distilled Predictive Distributions](https://mlsanity.com/paper/2206.07562/) (2022)
- [Bayesian Variational Federated Learning and Unlearning in Decentralized Networks](https://mlsanity.com/paper/2104.03834/) (2021)
- [Compressed Particle-Based Federated Bayesian Learning and Unlearning](https://mlsanity.com/paper/2209.07267/) (2022)
- [Personalized Federated Learning of Probabilistic Models: A PAC-Bayesian Approach](https://mlsanity.com/paper/2401.08351/) (2024)
- [Client Selection for Federated Bayesian Learning](https://mlsanity.com/paper/2212.05492/) (2022)
- [ELF: Federated Langevin Algorithms with Primal, Dual and Bidirectional Compression](https://mlsanity.com/paper/2303.04622/) (2023)
- [Long-time asymptotics of noisy SVGD outside the population limit](https://mlsanity.com/paper/2406.11929/) (2024)
- [A Bayesian Framework for Clustered Federated Learning](https://mlsanity.com/paper/2410.15473/) (2024)
- [Improved Stein Variational Gradient Descent with Importance Weights](https://mlsanity.com/paper/2210.00462/) (2022)
- [Federated Learning with Uncertainty and Personalization via Efficient Second-order Optimization](https://mlsanity.com/paper/2411.18385/) (2024)
- [Trustworthy Personalized Bayesian Federated Learning via Posterior Fine-Tune](https://mlsanity.com/paper/2402.16911/) (2024)
- [Variational Bayes for Federated Continual Learning](https://mlsanity.com/paper/2405.14291/) (2024)
- [Federated Generalised Variational Inference: A Robust Probabilistic Federated Learning Framework](https://mlsanity.com/paper/2502.00846/) (2025)
- [Personalized Bayesian Federated Learning with Wasserstein Barycenter Aggregation](https://mlsanity.com/paper/2505.14161/) (2025)
- [FedUP: Efficient Pruning-based Federated Unlearning for Model Poisoning Attacks](https://mlsanity.com/paper/2508.13853/) (2025)
- [Robust Distributed Bayesian Learning with Stragglers via Consensus Monte Carlo](https://mlsanity.com/paper/2112.09794/) (2021)
- [Be Bayesian by Attachments to Catch More Uncertainty](https://mlsanity.com/paper/2310.13027/) (2023)
- [Cooperation and Federation in Distributed Radar Point Cloud Processing](https://mlsanity.com/paper/2405.01995/) (2024)
- [Compressed Bayesian Federated Learning for Reliable Passive Radio Sensing in Industrial IoT](https://mlsanity.com/paper/2405.05855/) (2024)

## Abstract (arXiv)

This paper introduces Distributed Stein Variational Gradient Descent (DSVGD), a non-parametric generalized Bayesian inference framework for federated learning. DSVGD maintains a number of non-random and interacting particles at a central server to represent the current iterate of the model global posterior. The particles are iteratively downloaded and updated by one of the agents with the end goal of minimizing the global free energy. By varying the number of particles, DSVGD enables a flexible trade-off between per-iteration communication load and number of communication rounds. DSVGD is shown to compare favorably to benchmark frequentist and Bayesian federated learning strategies, also scheduling a single device per iteration, in terms of accuracy and scalability with respect to the number of agents, while also providing well-calibrated, and hence trustworthy, predictions.