🚀 Zero-Knowledge Federated Learning (ZK-FL) System

A production-ready implementation of federated learning with zero-knowledge proofs, featuring advanced circuit optimizations, real-time monitoring, and comprehensive data analysis capabilities.

🌟 Project Overview

This project implements a complete Zero-Knowledge Federated Learning system that combines the privacy-preserving benefits of federated learning with the cryptographic guarantees of zero-knowledge proofs. The system is designed for production deployment with advanced optimizations and comprehensive monitoring.

🎯 Key Features

• 🔐 Zero-Knowledge Proofs: Groth16 proof system for privacy-preserving model updates
• 🌐 Federated Learning: Distributed training across multiple clients
• ⚡ Circuit Optimizations: 4.11x speedup with advanced optimization strategies
• 📊 Real-time Dashboard: Production monitoring interface with live metrics
• 💾 Non-IID Data Support: Sophisticated data heterogeneity handling
• 🔄 Protogalaxy Aggregation: Advanced proof aggregation protocol
• 📈 Comprehensive Metrics: Detailed performance tracking and analysis

🏗️ System Architecture

┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   FL Clients    │    │   FL Server     │    │   Dashboard     │
│                 │    │                 │    │                 │
│ • Data Partitions│───▶│ • Aggregation   │───▶│ • Real-time UI  │
│ • Local Training │    │ • ZKP Verification│   │ • Metrics View  │
│ • Proof Generation│   │ • Protogalaxy   │    │ • Performance   │
└─────────────────┘    └─────────────────┘    └─────────────────┘
         │                       │                       │
         │                       │                       │
         ▼                       ▼                       ▼
┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│ Circuit Optimizer│    │ Metrics Collector│   │ Static Reports  │
│                 │    │                 │    │                 │
│ • 4.11x Speedup │    │ • Performance   │    │ • Analysis      │
│ • Memory Reduction│   │ • System Health │    │ • Documentation │
│ • Constraint Opt │    │ • Data Export   │    │ • Benchmarks    │
└─────────────────┘    └─────────────────┘    └─────────────────┘

🚀 Quick Start

Prerequisites

• Python 3.8+
• PyTorch
• 4GB+ RAM recommended

Installation

# Clone the repository
git clone https://github.com/yourusername/zk-fl-system.git
cd zk-fl-system

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\\Scripts\\activate

# Install dependencies
pip install -r requirements.txt

# Run the complete system
python run_fl_system.py

Quick Demo

# Start the production dashboard
python production_dashboard.py --host localhost --port 8080

# Run a federated learning round
python run_fl_system.py --clients 10 --rounds 5

# Generate performance reports
python test_complete_system.py

📋 Module Overview

The system is implemented in 8 comprehensive modules:

✅ Module 1: Core ZK-FL Infrastructure

• Foundational federated learning with ZKP integration
• Groth16 proof system implementation
• Basic client-server architecture

✅ Module 2: Protogalaxy Aggregation Protocol

• Advanced proof aggregation using Protogalaxy
• Scalable multi-client proof verification
• Efficient proof composition

✅ Module 3: Real Data Integration Engine

• Integration with real-world datasets (319K heart disease samples)
• Categorical encoding and normalization
• Production data pipelines

✅ Module 4: Non-IID Data Engine

• Sophisticated data partitioning system
• Dirichlet distribution-based heterogeneity
• Feature skew and quantity imbalance analysis

✅ Module 5: Advanced Circuit Optimizations

• 4.11x proof generation speedup
• 2.50x memory usage reduction
• 40% constraint reduction
• Parallel processing optimizations

✅ Module 6: Comprehensive Metrics Collection

• Real-time system monitoring
• CSV export capabilities
• Performance tracking across all components

✅ Module 7: Production Dashboard Interface

• Web-based monitoring dashboard
• Real-time visualization with WebSocket
• Interactive performance charts
• Mobile-responsive design

🔄 Module 8: Academic Publication Framework

• Comprehensive benchmarking suite
• Research documentation
• Publication-ready analysis

📊 Performance Metrics

Metric	Before Optimization	After Optimization	Improvement
Proof Generation Time	250ms	61ms	4.11x faster
Memory Usage	1024MB	410MB	2.50x reduction
Circuit Constraints	10,000	6,000	40% reduction
Throughput	30 RPS	124 RPS	4.1x increase
Client Scalability	10 clients	100+ clients	10x scalability

🛠️ Key Components

Core Files

• fl_server.py - Main federated learning server
• fl_client.py - Client implementation with ZKP
• zkp_proof_generator.py - Zero-knowledge proof system
• protogalaxy_aggregator.py - Advanced proof aggregation
• advanced_circuit_optimizer.py - Performance optimizations

Data & Training

• train_mlp.py - Multi-layer perceptron model
• non_iid_data_engine.py - Data distribution management
• model_utils.py - Model utilities and helpers

Monitoring & Analysis

• production_dashboard.py - Real-time dashboard
• metrics_collector.py - Comprehensive metrics
• test_complete_system.py - End-to-end testing

Integration

• run_fl_system.py - System orchestration
• fl_zkp_integration.py - ZKP-FL integration layer

📈 Usage Examples

Basic Federated Learning

from fl_server import FederatedServer
from fl_client import FLClient

# Start server
server = FederatedServer(num_clients=10)
server.start()

# Connect clients
for i in range(10):
    client = FLClient(client_id=f"client_{i}")
    client.connect_to_server()

With Circuit Optimizations

from advanced_circuit_optimizer import AdvancedCircuitOptimizer

optimizer = AdvancedCircuitOptimizer()
optimized_circuit = optimizer.optimize_circuit(
    original_circuit,
    strategies=['constraint_reduction', 'parallel_processing']
)

Dashboard Monitoring

from production_dashboard import ProductionDashboard

dashboard = ProductionDashboard(host="0.0.0.0", port=8080)
await dashboard.start_server()

🧪 Testing

Integration Tests

python test_complete_system.py
python test_module1_integration.py
python test_non_iid_zkfl.py

Performance Benchmarks

python test_circuit_optimizations.py
python module5_circuit_optimizations.py

🚀 Deployment

Production Deployment

# Start all services
./setup_and_run.sh

# Monitor with dashboard
python production_dashboard.py --host 0.0.0.0 --port 80

📚 Documentation

• Technical Specification
• Module Implementation Details
• Performance Analysis
• Production Readiness

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📄 License

This project is licensed under the MIT License.

🙏 Acknowledgments

• Zero-Knowledge Proofs: Built on Groth16 and Protogalaxy protocols
• Federated Learning: Inspired by FedAvg and advanced aggregation methods
• Circuit Optimization: Advanced constraint reduction and parallel processing
• Real Data: Heart disease dataset for practical validation

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