Camouflage Learning

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

Abstract

Federated learning has been proposed as a concept for distributed machine learning which enforces privacy by avoiding sharing private data with a coordinator or distributed nodes. However, information on local data might be leaked through the model updates. We propose Camouflage learning, a machine learning scheme that distributes both the data and the model. Neither the distributed devices nor the coordinator is at any point in time in possession of the complete model. Furthermore, data and model are obfuscated during distributed model inference and distributed model training. Camouflage learning can be implemented with various Machine learning schemes.

Original languageEnglish
Title of host publication2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2021
PublisherIEEE
Pages724-729
Number of pages6
ISBN (Electronic)9781665404242
DOIs
Publication statusPublished - 25 May 2021
MoE publication typeA4 Article in a conference publication
EventIEEE International Conference on Pervasive Computing and Communications Workshops - Kassel, Germany
Duration: 22 Mar 202126 Mar 2021
https://www.percom.org/

Publication series

NameIEEE international conference on pervasive computing and communications workshops
PublisherIEEE

Conference

ConferenceIEEE International Conference on Pervasive Computing and Communications Workshops
Abbreviated titlePerCom Workshops
CountryGermany
CityKassel
Period22/03/202126/03/2021
Internet address

Keywords

  • Distributed machine learning
  • Internet of Things
  • Multi-key homomorphic encryption
  • Privacy

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