README.md

Measure Energy of Flower FL in G5K

This project provides tools to measure the energy consumption of Flower-based federated learning (FL) experiments on the Grid'5000 (G5K) testbed. It includes scripts to manage distributed nodes, run FL experiments, and monitor energy usage.

Table of Contents

• Getting Started
• Installation
• Usage
  • FL framework
  • Configure instances for CPU
  • Configure instances for GPU
  • Run exp
• Quickstart
  • Step 1. Reserve the Hosts in G5K
  • Step 2. Configure
  • Step 3. Collect IP
  • Step 4. Run the Campaign or Instance
  • Step 5. Output
  • Step 6. Clean Up
• License

Getting Started

The repository includes an example of Flower (using TensorFlow) in the Flower_v1 directory and the source of measuring framework in Run. This example demonstrates how to use this framework to measure energy consumption.

Installation

Clone the repository and navigate to the eflwr directory:

git clone https://gitlab.irit.fr/sepia-pub/delight/eflwr.git
cd eflwr

This framework requires:

• Python 3.9.2 or higher.
• Additional dependencies listed in requirements.txt. Install them with:

  pip install -r requirements.txt

Note: requirements.txt includes tensorflow, tensorflow-datasets scikit-learn and numpy using for the provided Flower example.

Navigate to Run directory:

cd Run

Usage

FL Framework

FL scripts (including server and client scripts) can be updated, for example, in the Flower_v1 directory.

Important Notes on Flower Client Configuration

When using this framework for Flower deployment on distributed servers, the client script should not require manual input of the IP:PORT for the Flower server. The framework is already designed to handle this automatically.

Key Points: