Installation

Note: This code has been written and tested on Ubuntu and should work seamlessly on any Linux-based distribution. Windows users may need to adjust some steps.

📋 Prerequisites

  • Git
  • Python 3.x
  • pip (Python package installer)
  • Miniconda (recommended for environment management)

🚀 Getting Started

Follow these steps to set up and use this repository:

  1. Clone the Repository
git clone https://github.com/nclabteam/FedEasy.git

  1. After cloning, navigate to the cloned directory and open the terminal.

  2. Ensure pip is installed. If not, install it using:

 sudo apt install python3-pip
  1. We will use Miniconda to create a virtual environment. Download Miniconda with: (If you already have Conda installed, skip steps 4 and 5.)
 curl https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -o Miniconda3-latest-Linux-x86_64.sh
  1. Then install Miniconda using the command below:
  bash Miniconda3-latest-Linux-x86_64.sh
  1. Create a new virtual environment using Conda:
 conda env create -f environment.yaml

This will create a virtual environment named venv-fedeasy based on the environment.yaml file.

  1. Activate the virtual environment:
 conda deactivate
 conda activate venv-fedeasy

  1. You can change the configuration as per your needs in the config.yaml file.

  2. To scale clients to a few hundred, we can run Flower in simulation mode on a single machine like this:

python main.py
    or
python main.py --config /path/to/config.yaml

This script reads the configuration from the config.yaml file and starts the simulation.

The outputs will be saved in the out directory.