Compressed Particle-Based Federated Bayesian Learning and Unlearning

no code implementations • 14 Sep 2022 • Jinu Gong, Osvaldo Simeone, Joonhyuk Kang

Conventional frequentist FL schemes are known to yield overconfident decisions.

Quantization

Forget-SVGD: Particle-Based Bayesian Federated Unlearning

no code implementations • 23 Nov 2021 • Jinu Gong, Osvaldo Simeone, Rahif Kassab, Joonhyuk Kang

Variational particle-based Bayesian learning methods have the advantage of not being limited by the bias affecting more conventional parametric techniques.

Bayesian Inference Federated Learning

Bayesian Variational Federated Learning and Unlearning in Decentralized Networks

no code implementations • 8 Apr 2021 • Jinu Gong, Osvaldo Simeone, Joonhyuk Kang

Federated Bayesian learning offers a principled framework for the definition of collaborative training algorithms that are able to quantify epistemic uncertainty and to produce trustworthy decisions.

Federated Learning Variational Inference