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Federated Generalized Bayesian Learning via Distributed Stein Variational Gradient Descent

arXiv 2009.06419 ↗First posted 11 Sep 2020cs.LG cs.IT eess.SPcited by 34

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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.

Key facts. Cited by 34 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Sep 2020.

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Citing papers per year: 2021: 7; 2022: 5; 2023: 5; 2024: 10; 2025: 6; 2026: 1.

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  1. 2021Distributed Learning in Wireless Networks: Recent Progress and Future Challengescited by 125
  2. 2021Personalized Federated Learning with Gaussian Processescited by 76
  3. 2023GNNDelete: A General Strategy for Unlearning in Graph Neural Networkscited by 43
  4. 2021QLSD: Quantised Langevin stochastic dynamics for Bayesian federated learningcited by 26
  5. 2024Federated Unlearning: A Survey on Methods, Design Guidelines, and Evaluation Metricscited by 25
  6. 2023Bayesian Federated Learning: A Surveycited by 22
  7. 2021A Convergence Theory for SVGD in the Population Limit under Talagrand's Inequality T1cited by 21
  8. 2023Federated Learning as Variational Inference: A Scalable Expectation Propagation Approachcited by 17
  9. 2022Convergence of Stein Variational Gradient Descent under a Weaker Smoothness Conditioncited by 16
  10. 2021Forget-SVGD: Particle-Based Bayesian Federated Unlearningcited by 14
  11. 2022Federated Learning with Uncertainty via Distilled Predictive Distributionscited by 11
  12. 2021Bayesian Variational Federated Learning and Unlearning in Decentralized Networkscited by 8
22 more
  1. 2022Compressed Particle-Based Federated Bayesian Learning and Unlearningcited by 5
  2. 2024Personalized Federated Learning of Probabilistic Models: A PAC-Bayesian Approachcited by 5
  3. 2022Client Selection for Federated Bayesian Learningcited by 4
  4. 2023ELF: Federated Langevin Algorithms with Primal, Dual and Bidirectional Compressioncited by 4
  5. 2024A Bayesian Framework for Clustered Federated Learningcited by 3
  6. 2022Improved Stein Variational Gradient Descent with Importance Weightscited by 2
  7. 2024Long-time asymptotics of noisy SVGD outside the population limitcited by 2
  8. 2024Trustworthy Personalized Bayesian Federated Learning via Posterior Fine-Tunecited by 1
  9. 2024Variational Bayes for Federated Continual Learningcited by 1
  10. 2024Federated Learning with Uncertainty and Personalization via Efficient Second-order Optimizationcited by 1
  11. 2025Federated Generalised Variational Inference: A Robust Probabilistic Federated Learning Frameworkcited by 1
  12. 2025Personalized Bayesian Federated Learning with Wasserstein Barycenter Aggregationcited by 1
  13. 2025FedUP: Efficient Pruning-based Federated Unlearning for Model Poisoning Attackscited by 1
  14. 2021Robust Distributed Bayesian Learning with Stragglers via Consensus Monte Carlocited by 0
  15. 2023Be Bayesian by Attachments to Catch More Uncertaintycited by 0
  16. 2024Cooperation and Federation in Distributed Radar Point Cloud Processingcited by 0
  17. 2024Compressed Bayesian Federated Learning for Reliable Passive Radio Sensing in Industrial IoTcited by 0
  18. 2024Personalizing Low-Rank Bayesian Neural Networks Via Federated Learningcited by 0
  19. 2025Bayesian Federated Learning for Continual Trainingcited by 0
  20. 2025Cost-Free Personalization via Information-Geometric Projection in Bayesian Federated Learningcited by 0
  21. 2025Adaptive Kernel Selection for Stein Variational Gradient Descentcited by 0
  22. 2026Federated Martingale Posterior Sampingcited by 0