Extremely Randomized Trees With Privacy Preservation for Distributed Structured Health Data-Reference-Cited by-同舟云学术

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Extremely Randomized Trees With Privacy Preservation for Distributed Structured Health Data

Published:2022 Issue: Volume:10 Page:6010-6027
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Aminifar Amin^ORCID,Shokri Matin,Rabbi Fazle^ORCID,Pun Violet Ka I.,Lamo Yngve^ORCID

Funder

INTROducing Mental health through the Adaptive Technology (INTROMAT) Project

Norwegian Research Council

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science

Link

http://xplorestaging.ieee.org/ielx7/6287639/9668973/09676691.pdf?arnumber=9676691

Reference92 articles.

1. Random decision forests

2. A Hybrid Approach to Privacy-Preserving Federated Learning

3. Federated Forest

4. Greedy function approximation: A gradient boosting machine;friedman;Ann Statist,2001

5. Privacy preserving distributed extremely randomized trees

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