Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Privacy Preserving QoE Modeling using Collaborative Learning
Ericsson Research, SWE.
Ericsson Research, SWE.
Blekinge Institute of Technology, Faculty of Computing, Department of Technology and Aesthetics.
2019 (English)In: Proceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM, Association for Computing Machinery , 2019, p. 13-18Conference paper, Published paper (Refereed)
Abstract [en]

Machine Learning (ML) based Quality of Experience (QoE) models potentially suffer from over-fitting due to limitations including low data volume, and limited participant profiles. This prevents models from becoming generic. Consequently, these trained models may under-perform when tested outside the experimented population. One reason for the limited datasets, which we refer in this paper as small QoE data lakes, is due to the fact that often these datasets potentially contain user sensitive information and are only collected throughout expensive user studies with special user consent. Thus, sharing of datasets amongst researchers is often not allowed. In recent years, privacy preserving machine learning models have become important and so have techniques that enable model training without sharing datasets but instead relying on secure communication protocols. Following this trend, in this paper, we present Round-Robin based Collaborative Machine Learning model training, where the model is trained in a sequential manner amongst the collaborated partner nodes. We benchmark this work using our customized Federated Learning mechanism as well as conventional Centralized and Isolated Learning methods. © 2019 Association for Computing Machinery.

Place, publisher, year, edition, pages
Association for Computing Machinery , 2019. p. 13-18
Keywords [en]
Distributed Learning, Federated Learning, QoE, Convolutional codes, Data communication systems, Data privacy, Machine learning, Collaborative learning, Learning mechanism, Machine learning models, Quality of experience (QoE), Sensitive informations, Sequential manners, Quality of service
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:bth-18936DOI: 10.1145/3349611.3355548Scopus ID: 2-s2.0-85074785270ISBN: 9781450369275 (print)OAI: oai:DiVA.org:bth-18936DiVA, id: diva2:1371894
Conference
4th Workshop on QoE-Based Analysis and Management of Data Communication Networks, Internet-QoE 2019, co-located with MobiCom 2019, Los Cabos; Mexico, 21 October
Available from: 2019-11-21 Created: 2019-11-21 Last updated: 2019-11-21Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records BETA

Fiedler, Markus

Search in DiVA

By author/editor
Fiedler, Markus
By organisation
Department of Technology and Aesthetics
Computer Sciences

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 2 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf