Federated Learning with Raspberry Pi

This repository contains the code to set up a federated learning system using a Raspberry Pi as the client. Federated learning allows multiple devices to collaboratively learn a shared model while keeping the training data on the device.

Repository Structure

• client/: Contains the code for the Raspberry Pi client.
• server/: Contains the code for the central server that coordinates federated learning.
• README.md: This file, providing an overview and setup instructions for the project.

Requirements

General Requirements

• Python 3.7+
• TensorFlow or PyTorch
• flwr (Flower framework for federated learning)

Client Requirements (Raspberry Pi)

• Raspberry Pi (preferably the latest model)
• client/requirements.txt: Required Python packages for the client

Server Requirements

• Sufficient computational resources to run the federated learning server
• server/requirements.txt: Required Python packages for the server

Setup Instructions

Server Setup

1. Navigate to the server/ directory.
2. Install the required packages:

   pip install -r requirements.txt

3. Run the server:

   python server.py

Client Setup (Raspberry Pi)

1. Navigate to the client/ directory.
2. Install the required packages:

   pip install -r requirements.txt

3. Run the client:

   python client.py

Configuration

Client Configuration (client/config.py)

• SERVER_ADDRESS: The address of the federated learning server.
• DATASET_PATH: The path to the dataset used by the client.

Server Configuration (server/config.py)

• SERVER_ADDRESS: The address for the federated learning server to listen on.

Utility Functions

Server Utilities (server/utils.py)

• setup_logging(log_file="server.log"): Sets up logging to a file and console.
• log_info(message): Logs an informational message.
• log_error(message): Logs an error message.
• save_model(model, model_dir="models", model_name="model.h5"): Saves the model to the specified directory.
• load_model(model_dir="models", model_name="model.h5"): Loads the model from the specified directory.
• save_config(config, config_path="config.json"): Saves the configuration to a JSON file.
• load_config(config_path="config.json"): Loads the configuration from a JSON file.

Running the Federated Learning System

1. Start the Server: Ensure the server is running on a machine with sufficient resources.
2. Start the Client: Run the client script on the Raspberry Pi.
3. The server and client will communicate to perform federated learning.

References

• Flower Framework
• Federated Learning Concepts

Example Usage

Starting the Server

cd server/
pip install -r requirements.txt
python server.py