Compressed Particle-Based Federated Bayesian Learning and Unlearning

Jinu Gong, Osvaldo Simeone, Joonhyuk Kang

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Conventional frequentist federated learning (FL) schemes are known to yield overconfident decisions. Bayesian FL addresses this issue by allowing agents to process and exchange uncertainty information encoded in distributions over the model parameters. However, this comes at the cost of a larger per-iteration communication overhead. This letter investigates whether Bayesian FL can still provide advantages in terms of calibration when constraining communication bandwidth. We present compressed particle-based Bayesian FL protocols for FL and federated 'unlearning' that apply quantization and sparsification across multiple particles. The experimental results confirm that the benefits of Bayesian FL are robust to bandwidth constraints.

Original languageEnglish (US)
Pages (from-to)556-560
Number of pages5
JournalIEEE Communications Letters
Volume27
Issue number2
DOIs
StatePublished - Feb 1 2023

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Computer Science Applications
  • Modeling and Simulation

Keywords

  • Bayesian learning
  • Federated learning
  • machine unlearning
  • stein variational gradient descent
  • wireless communication

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