Malmö University Publications
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
Leveraging Federated Learning & Blockchain to counter Adversarial Attacks in Incremental Learning
Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).ORCID iD: 0000-0003-4071-4596
Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).ORCID iD: 0000-0003-0546-072X
Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).ORCID iD: 0000-0002-9471-8405
Show others and affiliations
2020 (English)In: IoT '20 Companion: 10th International Conference on the Internet of Things Companion, ACM Digital Library, 2020, p. 1-5, article id 2Conference paper, Published paper (Refereed)
Abstract [en]

Whereas data labelling in IoT applications is costly, it is also time consuming to train a supervised Machine Learning (ML) algorithm. Hence, a human oracle is required to gradually annotate the data patterns at run-time to improve the models’ learning behavior, through an active learning strategy in form of User Feedback Process (UFP). Consequently, it is worth to note that during UFP there may exist malicious content that may subject the learning model to be vulnerable to adversarial attacks, more so, manipulative attacks. We argue in this position paper, that there are instances during incremental learning, where the local data model may present wrong output, if retraining is done using data that has already been subjected to adversarial attack. We propose a Distributed Interactive Secure Federated Learning (DISFL) framework that utilizes UFP in the edge and fog node, that subsequently increases the amount of labelled personal local data for the ML model during incremental training. Furthermore, the DISFL framework addresses data privacy by leveraging federated learning, where only the model's knowledge is moved to a global unit, herein referred to as Collective Intelligence Node (CIN). During incremental learning, this would then allow the creation of an immutable chain of data that has to be trained, which in its entirety is tamper-free while increasing trust between parties. With a degree of certainty, this approach counters adversarial manipulation during incremental learning in active learning context at the same time strengthens data privacy, while reducing the computation costs.

Place, publisher, year, edition, pages
ACM Digital Library, 2020. p. 1-5, article id 2
Keywords [no]
Federated learning, adversarial, blockchain, privacy, incremental training
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:mau:diva-48196DOI: 10.1145/3423423.3423425ISI: 001062649200002Scopus ID: 2-s2.0-85117542476ISBN: 9781450388207 (electronic)OAI: oai:DiVA.org:mau-48196DiVA, id: diva2:1620287
Conference
10th International Conference on the Internet of Things Companion, October 6-9, 2020, Malmö Sweden
Funder
Knowledge FoundationAvailable from: 2021-12-15 Created: 2021-12-15 Last updated: 2023-12-13Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopusJournal website

Authority records

Kebande, Victor R.Alawadi, SadiBugeja, JosephPersson, Jan A.Olsson, Carl Magnus

Search in DiVA

By author/editor
Kebande, Victor R.Alawadi, SadiBugeja, JosephPersson, Jan A.Olsson, Carl Magnus
By organisation
Department of Computer Science and Media Technology (DVMT)
Computer Sciences

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 34 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