FederatedCryptix is an innovative and modular framework designed for federated learning with a strong focus on cryptographic security.
Project description
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
- Install dependencies:
pip install -r requirements.txt
Project details
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