federated-learning-framework 0.0.1

A modular and extensible framework for federated learning applications.

Federated Learning Framework

Overview

Welcome to the Federated Learning Framework, a modular and extensible solution for implementing federated learning across various applications. Harness the power of collective intelligence, ensure data privacy with homomorphic encryption, and apply it to domains like NLP, autonomous vehicles, drones, and more.

Features

• Modular and Extensible: Easily customizable for different machine learning and deep learning applications.
• Secure: Utilizes homomorphic encryption to ensure data privacy.
• Active Learning: Incorporates active learning strategies to improve model performance.
• Flexible Communication: Supports various connection methods including socket programming.
• Customizable: Users can edit and control every part of the framework with various functions.

Package Structure

federated_learning_framework/
│
├── README.md
├── setup.py
├── requirements.txt
├── federated_learning_framework/
│   ├── __init__.py
│   ├── central_server.py
│   ├── client_device.py
│   ├── encryption.py
│   ├── active_learning.py
│   ├── connection.py
│   ├── decorators.py
│   └── utils.py
└── tests/
    ├── __init__.py
    ├── test_central_server.py
    ├── test_client_device.py
    ├── test_encryption.py
    ├── test_active_learning.py
    └── test_utils.py

Detailed Component Description
Central Server
File: central_server.py

The central server orchestrates the federated learning process by coordinating the communication and aggregation of model weights from various client devices.

Key Functions:

run_server: Starts the server to handle client connections.
handle_client: Manages incoming messages from clients.
transmit_weights: Broadcasts the aggregated weights to clients.
send_data_to_client: Sends specific data to a client.
get_data_from_client: Requests and receives data from a client.
query_active_learning: Implements active learning strategies to select data for labeling.
Client Device
File: client_device.py

Client devices perform local training on their datasets and communicate with the central server.

Key Functions:

connect_to_central_server: Connects to the central server.
federated_learning: Coordinates local training and communication with the server.
receive_weights: Receives model weights from the central server.
send_weights: Sends model weights to the central server.
receive_data: Receives data from the central server.
Encryption
File: encryption.py

Provides functions for creating encryption contexts and encrypting/decrypting model weights.

Key Functions:

create_context: Sets up the encryption context using TenSEAL.
encrypt_weights: Encrypts model weights.
decrypt_weights: Decrypts encrypted model weights.
Active Learning
File: active_learning.py

Implements active learning strategies to enhance the training process by selectively querying informative data points.

Key Functions:

select_informative_samples: Selects samples for labeling based on uncertainty.
Connection
File: connection.py

Manages the connection types and protocols (e.g., WebSocket) for communication between the central server and client devices.

Key Functions:

run_server: Starts a WebSocket server.
connect_to_server: Establishes a WebSocket connection to the server.
Decorators
File: decorators.py

Provides decorators for adding federated learning and encryption functionalities to functions.

Key Functions:

federated_learning_decorator: Wraps a function to enable federated learning.
encryption_decorator: Wraps a function to enable homomorphic encryption.
Utilities
File: utils.py

Includes utility functions used throughout the framework.

Installation
Clone the repository:

bash
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git clone https://github.com/mehrdaddjavadi/federated_learning_framework.git
Navigate to the directory:

bash
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cd federated_learning_framework
Install the dependencies:

bash
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pip install -r requirements.txt
Usage
Setting Up the Central Server
python
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import asyncio
from federated_learning_framework.central_server import CentralServer

async def main():
    server = CentralServer()
    await server.run_server()

asyncio.run(main())
Setting Up a Client Device
python
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import asyncio
import tensorflow as tf
from federated_learning_framework.client_device import ClientDevice
from federated_learning_framework.encryption import create_context

# Define your model
model = tf.keras.Sequential([
    tf.keras.layers.Dense(4, activation='relu', input_shape=(3072,)),
    tf.keras.layers.Dense(10, activation='softmax')
])

# Create context for encryption
context = create_context()

# Initialize the client device
client = ClientDevice(client_id=1, model=model, context=context)

async def main():
    uri = "ws://localhost:8089"
    await client.connect_to_central_server(uri)
    x_train, y_train = ...  # Load your training data
    await client.federated_learning(uri, x_train, y_train)
    # Optionally receive data from central server
    data = await client.receive_data()
    print(f"Received data: {data}")

asyncio.run(main())
Using Decorators
python
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import asyncio
import tensorflow as tf
from federated_learning_framework.decorators import federated_learning_decorator, encryption_decorator
from federated_learning_framework.client_device import ClientDevice
from federated_learning_framework.encryption import create_context

# Create context for encryption
context = create_context()

# Define your model
model = tf.keras.Sequential([
    tf.keras.layers.Dense(4, activation='relu', input_shape=(3072,)),
    tf.keras.layers.Dense(10, activation='softmax')
])

@federated_learning_decorator(uri="ws://localhost:8089")
@encryption_decorator(context=context)
async def main():
    client = ClientDevice(client_id=1, model=model, context=context)
    await client.connect_to_central_server('ws://localhost:8089')
    x_train, y_train = ...  # Load your training data
    await client.federated_learning('ws://localhost:8089', x_train, y_train)

asyncio.run(main())
Running Tests
To run the tests, execute the following command in the root directory:

bash
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python -m unittest discover -s tests
License
The usage of this library is free for academic work with proper referencing. For business, governmental, and any other types of usage, please contact me directly. All rights are reserved.

Contact: [e-mail]

Contributing
Feel free to contribute by submitting a pull request or opening an issue.