/FederatedCryptix

FederatedCryptix is an innovative and modular framework designed for federated learning with a strong focus on cryptographic security.

Primary LanguagePython

FederatedCryptix

Overview

This repository contains a federated learning framework that enables collaborative training of machine learning models across multiple devices while preserving data privacy.

Folder Structure

  • config/: Configuration files for models and training parameters.
  • encryption/: Contains encryption and decryption logic using TenSEAL.
  • models/: Model implementations for TensorFlow and PyTorch.
  • communication/: Manages WebSocket communication.
  • server/: Central server implementation.
  • clients/: Client device implementation.
  • utils/: Utility functions and decorators.
  • logs/: Log files for server and clients.
  • main_server.py: Entry point to start the central server.
  • main_client.py: Entry point to start a client device.

Getting Started

Central Server

  1. Install dependencies:
    pip install -r requirements.txt