This repository contains the Federated Learning Demonstrator, a comprehensive suite of tools designed for machine learning applications in a Federated context. This server component that handles the orchestration and notification of all training participants and ensuring smooth and efficient operation.
The demonstrator further provides capabilities for diverse model aggregation methods, merging the model inputs from each participant. It also offers model inference, enabling the generation of predictions using the trained models. The repository also includes utilities for quantifying uncertainty, which provide a measure of the reliability of the model's predictions.
This project is the server component of the Federated Learning (FL) platform, serving as a proof of concept for the Catena-X project. The FL platform aims to demonstrate the potential of federated learning in a practical, real-world context.
For a comprehensive understanding of the FL platform, please refer to the official FL platform documentation.
A complete list of all repositories relevant to the FL platform can be found here.
This README.md is primarily intended for developers and contributors, providing necessary information for setup, installation, and contribution guidelines. If you're interested in using or testing this project, we recommend starting with the GitHub pages. They offer a more user-friendly interface and comprehensive guides to get you started.
sudo apt install python<version>
#If not available you can add latest versions with
sudo add-apt-repository ppa:deadsnakes/ppa #Install venv
sudo apt install python<version>-venv#python 3.10 or later
which python
#virtualenv or venv
pip install -U virtualenv# create virtual environment
virtualenv -p $(which python<version>) .venv
# or
python<version> -m venv .venv
# activate our virtual environment
source .venv/bin/activate
# update pip (optional)
python -m pip install -U pip
# install
./dev install -U -e ".[all]"$ ./dev --help
usage: ./dev <action> [options]
positional arguments:
{celery,clean,collectstatic,coverage,coverage-report,db-reset,doc,doc-build,docker-build,help,install,licenses,licenses-check,lint,lint-code,lint-doc,lint-scripts,makemigrations,manage,migrate,mypy,safety-check,start,superuser,test,version,versions}
Available sub commands
help Show this help message and exit
start Run the application
celery Run celery worker
migrate Run database migrations
makemigrations Create new database migrations
manage Run django manage.py
superuser Create superuser
collectstatic Collect static files
db-reset Reset database
docker-build Build docker images for local development
test Run all tests
lint Run all linter
lint-code Run code linter
lint-doc Run documentation linter
lint-scripts Run bash script linter
mypy Run type checker
coverage Run unit tests
coverage-report Generate test coverage report
doc Start documentation server
doc-build Build documentation
licenses Generate licenses
licenses-check Check licenses
safety-check Check dependencies for known security vulnerabilities
install Install package
clean Clean up local files
version Show package version
versions Show versions
options:
--no-http-serve Do not serve the action result via HTTP- Type-Save and linting with mypy+flake8
- Scripts and examples for linux, wsl (bash)
This projects is using the Docstring style from Google. At least public classes, methods, fields, ... should be documented.
"""
This is the single line short description.
This is the multiline or long description.
Note, that the whole Docstring support markdown styling.
The long description can also contains multiple paragraphs.
Args:
log_filepath (str): Log file path.
ensure_log_dir (bool, optional): Create directory for the log file if not exists. Defaults to True.
Returns:
Dict[str, Any]: logging configuration dict
"""
