Federated-Learning-Orchestrator
🔗 Privacy-Preserving Federated Learning for Mobile Devices
Coordinate distributed AI training across devices without centralized data sharing
🚀 What It Does
- 🔗 Multi-Device Coordination - Orchestrates federated learning across mobile devices
- 🔒 Differential Privacy - Protects individual data with mathematical privacy guarantees
- 📱 Mobile-First Architecture - Lightweight coordination optimized for Android devices
- 🛡️ Cryptographic Security - HMAC signatures and privacy budget management
⚡ Quick Start
# Install NumPy (only external dependency)
pip install numpy
# Run the federated learning demo
python federated_learning_orchestrator.py
🎯 Key Features
| Feature | Description |
|---|---|
| Node Coordination | Manages federated participants and coordinators |
| Privacy Engine | Differential privacy with budget allocation |
| Model Aggregation | Federated averaging with Byzantine fault tolerance |
| Mobile Optimization | Efficient SQLite storage and lightweight crypto |
📊 Demo Results
=== Federated Learning Orchestrator MVP Demo ===
✓ Coordinator initialized with ID: b3f64510
✓ Added 5 participants with mobile optimization
--- Round 1 ---
✓ Round 1 completed successfully
Privacy Budget Status:
participant_1: 9.00/10.00 remaining
participant_2: 9.00/10.00 remaining
Final Results:
Successful Rounds: 18/18
Total Model Updates: 54
Privacy Budget Management: ✓ Active
🎯 Enterprise Use Cases
- Healthcare AI - Train medical models across hospitals without sharing patient data
- Financial Services - Fraud detection models without exposing transaction data
- IoT Networks - Edge AI learning across distributed sensors
- Mobile Apps - Personalized models without centralized user data
🛡️ Privacy & Security
- 🔒 Differential Privacy - Mathematical privacy guarantees with epsilon-delta framework
- 📊 Privacy Budgets - Automatic budget allocation and exhaustion prevention
- 🔐 Cryptographic Signatures - HMAC-based model update verification
- 📱 Local Processing - No raw data leaves participant devices
📱 Requirements
Python 3.7+
NumPy (pip install numpy)
SQLite3 (included with Python)
Tested on: Samsung Galaxy S24 with Pydroid 3
📁 Files
federated_learning_orchestrator.py- Complete federated learning systemREADME.md- Full technical documentationLICENSE- MIT License
👤 About the Author
Justin Lane
🔗 GitHub: @aiwithjusl
🔗 LinkedIn: Justin Lane
📬 Email: aiwithjusl.dev@gmail.com
**⭐ Star this repo if you find it useful! ⭐**
*Built for enterprise AI deployment and privacy-compliant distributed learning.*