Federated Generalized Bayesian Learning via Distributed Stein Variational Gradient Descent

Rahif Kassab, Osvaldo Simeone

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


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 a subset of 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 in terms of accuracy and scalability with respect to the number of agents, while also providing well-calibrated, and hence trustworthy, predictions.

Original languageEnglish (US)
Pages (from-to)2180-2192
Number of pages13
JournalIEEE Transactions on Signal Processing
StatePublished - 2022

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Electrical and Electronic Engineering


  • Federated learning
  • bayesian learning
  • variational inference


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