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HFedRF: Horizontal Federated Random Forest
Dalarna University, School of Information and Engineering, Microdata Analysis.ORCID iD: 0000-0001-9523-6689
2024 (English)In: International Congress and Workshop on Industrial AI and eMaintenance 2023. IAI 2023. Lecture Notes in Mechanical Engineering. / [ed] Kumar, U., Karim, R., Galar, D., Kour, R, Springer Science and Business Media Deutschland GmbH , 2024, p. 409-422Conference paper, Published paper (Refereed)
Abstract [en]

Real-world data is typically dispersed among numerous businesses or governmental agencies, making it difficult to integrate them into data privacy laws like the General Data Protection Regulation of the European Union (GDPR). Two significant obstacles to the use of machine learning models in applications are the existence of such data islands and privacy issues. In this paper, we address these issues and propose ‘HFedRF: Horizontal Federated Random Forest’, a privacy-preserving federated model which is approximately lossless. Our proposed algorithm merges d random forests computed on d different devices and returns a global random forest which is used for prediction on local devices. In our methodology, we compare IIDs (Independent and Identically Distributed) and non-IIDs variant of our algorithm HFedRF with traditional machine learning (ML) methods i.e., decision tree and random forest. Our results show that we achieve benchmark comparable results with our algorithm for IID as well as non-IID settings of federated learning. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Place, publisher, year, edition, pages
Springer Science and Business Media Deutschland GmbH , 2024. p. 409-422
Keywords [en]
Decision trees, Federated learning, Merge random forest, Merge trees, Random forest, Learning algorithms, Machine learning, Privacy-preserving techniques, Business agencies, General data protection regulations, Governmental agency, Merge tree, Privacy law, Random forests, Real-world
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:du-47912DOI: 10.1007/978-3-031-39619-9_30Scopus ID: 2-s2.0-85181980747ISBN: 9783031396182 (electronic)OAI: oai:DiVA.org:du-47912DiVA, id: diva2:1831338
Conference
7th International Congress and Workshop on Industrial AI and eMaintenance, IAI 2023, Luleå 13 June 2023 through 15 June 2023
Available from: 2024-01-25 Created: 2024-01-25 Last updated: 2024-01-25Bibliographically approved

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Mehra, Priyanka

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Total: 97 hits
CiteExportLink to record
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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • chicago-author-date
  • chicago-note-bibliography
  • 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