🌼 Federated Learning with Flower

Welcome to the Flower Federated Learning Project! This repository demonstrates the implementation of a federated learning system using the Flower framework. Federated learning enables collaborative model training across decentralized devices while ensuring data privacy.

🚀 Features

• Privacy-Preserving Training: Train models without sharing raw data.
• Customizable: Easily adapt the code for different machine learning models and datasets.
• Scalable: Supports various scenarios from small setups to large-scale federated systems.

🛠️ Requirements

• Python 3.8+
• Flower (pip install flwr)

📂 Project Structure

• Flower_federated_learning.ipynb: Main notebook for federated learning experiments.
• requirements.txt: List of required libraries.
• Additional files and resources for running the project.