Joint Privacy Enhancement and Quantization in Federated Learning.

Natalie Lang, Nir Shlezinger

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Federated learning (FL) is an emerging paradigm for training machine learning models using possibly private data available at edge devices. Among the key challenges associated with FL are first the need to preserve the privacy of the local data sets, and second the communication load due to the repeated exchange of updated models; both are often tackled individually with methods whose operation distorts the updated models, e.g., local differential privacy (LDP) mechanisms and lossy compres- sion, respectively. In this work we propose a method for joint privacy enhancement and quantization (JoPEQ), unifying lossy compression and privacy enhancement for FL. JoPEQ utilizes universal vector quantization, where distortion is statistically equivalent to additive noise, and augments the compression distortion with dedicated privacy preserving noise to simultaneously achieve compression and a desired privacy level. We numerically demonstrate that JoPEQ reduces the overall distortion compared to individual LDP and compression, which is translated into improved trained models.
Original languageEnglish
Title of host publication2022 IEEE International Symposium on Information Theory, ISIT 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2040-2045
Number of pages6
ISBN (Electronic)9781665421591
DOIs
StatePublished - 2022
Event2022 IEEE International Symposium on Information Theory, ISIT 2022 - Espoo, Finland
Duration: 26 Jun 20221 Jul 2022

Publication series

NameIEEE International Symposium on Information Theory - Proceedings
Volume2022-June
ISSN (Print)2157-8095

Conference

Conference2022 IEEE International Symposium on Information Theory, ISIT 2022
Country/TerritoryFinland
CityEspoo
Period26/06/221/07/22

Keywords

  • Training
  • Privacy
  • Vector quantization
  • Stochastic processes
  • Reliability theory
  • Distortion
  • Collaborative work

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Information Systems
  • Modeling and Simulation
  • Applied Mathematics

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