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
Byzantine-Robust Aggregation in Federated Learning Empowered Industrial IoT
Uppsala University, Sweden.ORCID iD: 0000-0003-0145-3127
University of Hong Kong, Hong Kong.ORCID iD: 0000-0002-3454-8731
RISE Research Institutes of Sweden, Digital Systems, Data Science. Uppsala University, Sweden.ORCID iD: 0000-0002-2586-8573
2023 (English)In: IEEE Transactions on Industrial Informatics, ISSN 1551-3203, E-ISSN 1941-0050, Vol. 19, no 2, p. 1165-Article in journal (Refereed) Published
Abstract [en]

Federated Learning (FL) is a promising paradigm to empower on-device intelligence in Industrial Internet of Things(IIoT) due to its capability of training machine learning models across multiple IIoT devices, while preserving the privacy of their local data. However, the distributed architecture of FL relies on aggregating the parameter list from the remote devices, which poses potential security risks caused by malicious devices. In this paper, we propose a flexible and robust aggregation rule, called Auto-weighted Geometric Median (AutoGM), and analyze the robustness against outliers in the inputs. To obtain the value of AutoGM, we design an algorithm based on alternating optimization strategy. Using AutoGM as aggregation rule, we propose two robust FL solutions, AutoGM_FL and AutoGM_PFL. AutoGM_FL learns a shared global model using the standard FL paradigm, and AutoGM_PFL learns a personalized model for each device. We conduct extensive experiments on the FEMNIST and Bosch IIoT datasets. The experimental results show that our solutions are robust against both model poisoning and data poisoning attacks. In particular, our solutions sustain high performance even when 30% of the nodes perform model or 50% of the nodes perform data poisoning attacks.

Place, publisher, year, edition, pages
2023. Vol. 19, no 2, p. 1165-
Keywords [en]
Electrical and Electronic Engineering, Computer Science Applications, Information Systems, Control and Systems Engineering, Computer Sciences, Datavetenskap (datalogi)
National Category
Communication Systems
Identifiers
URN: urn:nbn:se:ri:diva-56965DOI: 10.1109/tii.2021.3128164OAI: oai:DiVA.org:ri-56965DiVA, id: diva2:1612809
Available from: 2021-11-19 Created: 2021-11-19 Last updated: 2023-06-08Bibliographically approved

Open Access in DiVA

fulltext(2632 kB)796 downloads
File information
File name FULLTEXT01.pdfFile size 2632 kBChecksum SHA-512
819c5f02f57ce560ef0fbc3726113233769b2a7da8ab0a28170c0642ded66f996d6b88a75deac90e2fefc319e4ec8d83bbf7cdad14e6e93337c89f2352a11b18
Type fulltextMimetype application/pdf

Other links

Publisher's full text

Authority records

Voigt, Thiemo

Search in DiVA

By author/editor
Li, ShenghuiNgai, EdithVoigt, Thiemo
By organisation
Data Science
In the same journal
IEEE Transactions on Industrial Informatics
Communication Systems

Search outside of DiVA

GoogleGoogle Scholar
Total: 796 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 2571 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