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fedheonn_ensemble

Added bagging (Random Patches), parallelization and client/server evaluation (through FastAPI) to the base FedHEONN code.

https://github.com/abeludc93/fedheonn_ensemble

Science Score: 44.0%

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    CITATION.cff file
    Found CITATION.cff file
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    Scientific vocabulary similarity
    Low similarity (3.8%) to scientific vocabulary
Last synced: 6 months ago · JSON representation ·

Repository

Added bagging (Random Patches), parallelization and client/server evaluation (through FastAPI) to the base FedHEONN code.

Basic Info
  • Host: GitHub
  • Owner: abeludc93
  • Language: Python
  • Default Branch: master
  • Size: 37.9 MB
Statistics
  • Stars: 0
  • Watchers: 1
  • Forks: 0
  • Open Issues: 1
  • Releases: 0
Created over 1 year ago · Last pushed 11 months ago
Metadata Files
Readme Citation

README.md

FEDHEONN_ENSEMBLE

Added ensemble capabilities (via the Random Patches bagging technique) to the original FEDHEONN algorithm and implemented a server/client REST API architecture (using FastAPI) for federated learning scenearios.

Python

Developed using Python_3.10.7

Virtual Environment

To create the virtual environment follow these steps: mkdir -p <path> cd <path> virtualenv . To load and activate said environment: . bin/activate

Installing dependencies

Requirements: pip3 install -r requirements.txt

Owner

  • Login: abeludc93
  • Kind: user

Citation (CITATION.cff) 

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
- family-names: "Abel"
  given-names: "Pampín Rodríguez"
- family-names: "Óscar"
  given-names: "Fontenla Romero"
title: "FedHEONN Ensemble"
version: 1.0.0
date-released: 2024-09-24
url: "https://github.com/abeludc93/fedheonn\_ensemble"

GitHub Events

Total
  • Push event: 17
Last Year
  • Push event: 17

Dependencies

requirements.txt pypi
  • fastapi ==0.110.0
  • matplotlib ==3.7.2
  • numpy ==1.25.2
  • openpyxl ==3.1.2
  • pandas ==2.0.3
  • psutil ==5.9.2
  • pydantic ==2.8.2
  • pyqt5 ==5.15.9
  • python-multipart ==0.0.9
  • requests ==2.31.0
  • scikit-learn ==1.3.0
  • scipy ==1.11.1
  • tenseal ==0.3.14
  • ucimlrepo ==0.0.7
  • uvicorn ==0.22.0