vertical-federated-logistic-regression
This is a simple implementation of Vertically Federated Logistic Regression
https://github.com/afsana2910/vertical-federated-logistic-regression
Science Score: 54.0%
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✓CITATION.cff file
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✓codemeta.json file
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○Scientific vocabulary similarity
Low similarity (5.0%) to scientific vocabulary
Repository
This is a simple implementation of Vertically Federated Logistic Regression
Basic Info
Statistics
- Stars: 1
- Watchers: 1
- Forks: 1
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
Vertical Federated Logistic Regression
This is a simple implementation of Vertically Federated Logistic Regression.
A slight modification has been made to this algorithm in Line 11. Instead of allowing the guest to compute the gradients, it can only calculate the loss and share it with the hosts. The hosts compute the gradients on their end using the loss value received from the guest. From experiments, it was found that this modification in the algorithm gives better results.
Source: Federated learning on non-IID data: A survey
NB: Citing this GitHub repository would be highly appreciated if used for any research purpose.
Owner
- Name: Afsana Khan
- Login: Afsana2910
- Kind: user
- Location: Maastricht, Netherlands
- Company: Maastricht University
- Website: https://sites.google.com/site/afsanakhancse
- Repositories: 1
- Profile: https://github.com/Afsana2910
PhD Candidate
Citation (CITATION.cff)
# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!
cff-version: 1.2.0
title: Vertical federated learning
message: >-
Please cite this repository if used for any research
purpose
type: software
authors:
- given-names: Afsana
family-names: Khan
email: a.khan@maastrichtuniversity.nl
affiliation: Maastricht University
orcid: 'https://orcid.org/0000-0001-7615-3554'
repository-code: 'https://github.com/Afsana2910/Vertical-Federated-Learning'
abstract: >-
A simple implementation of Vertically Federated Logistic
Regression
keywords:
- vertical federated learning
- privacy preserving machine learning
- distributed learning