@inbook{6784b7bac2e4404d888909710ab0e81d,
title = "Forget-SVGD: Particle-Based Bayesian Federated Unlearning",
abstract = "Variational particle-based Bayesian learning methods have the advantage of not being limited by the bias affecting more conventional parametric techniques. This paper proposes to leverage the flexibility of non-parametric Bayesian approximate inference to develop a novel Bayesian federated unlearning method, referred to as Forget-Stein Variational Gradient Descent (Forget-SVGD). Forget-SVGD builds on SVGD - a particle-based approximate Bayesian inference scheme using gradient-based deterministic updates - and on its distributed (federated) extension known as Distributed SVGD (DSVGD). Upon the completion of federated learning, as one or more participating agents request for their data to be 'forgotten', Forget-SVGD carries out local SVGD updates at the agents whose data need to be 'unlearned', which are interleaved with communication rounds with a parameter server. The proposed method is validated via performance comparisons with non-parametric schemes that train from scratch by excluding data to be forgotten, as well as with existing parametric Bayesian unlearning methods. ",
keywords = "Bayesian learning, Federated learning, Machine unlearning, Stein variational gradient descent",
author = "Jinu Gong and Joonhyuk Kang and Osvaldo Simeone and Rahif Kassab",
note = "Funding Information: ‡This work was supported by the European Research Council (ERC) under the European Union{\textquoteright}s Horizon 2020 research and innovation programme (grant agreement No. 725731). Funding Information: ∗This work was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No.2018-0-00831, A Study on Physical Layer Security for Heterogeneous Wireless Network). Funding Information: This work was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No.2018-0-00831, A Study on Physical Layer Security for Heterogeneous Wireless Network). This work was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2020-0-01787) supervised by the IITP (Institute of Information & Communications Technology Planning & Evaluation). This work was supported by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No. 725731). Funding Information: †This work was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2020-0-01787) supervised by the IITP (Institute of Information & Communications Technology Planning & Evaluation). Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE Data Science and Learning Workshop, DSLW 2022 ; Conference date: 22-05-2022 Through 23-05-2022",
year = "2022",
doi = "10.1109/DSLW53931.2022.9820602",
language = "English",
series = "2022 IEEE Data Science and Learning Workshop, DSLW 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2022 IEEE Data Science and Learning Workshop, DSLW 2022",
address = "United States",
}