🚀 Federated Learning Simulation Framework

A comprehensive, production-ready federated learning simulation framework with advanced efficiency metrics, testing, and visualization capabilities.

Author: Rahul Kavati
Status: 🟢 Production Ready with Comprehensive Testing

🌟 Key Features

• 🔬 FL Simulation: Complete federated learning workflow with synthetic health data
• 📊 Efficiency Metrics: Comprehensive performance analysis and benchmarking
• 🧪 Testing Framework: 80%+ code coverage with unit, integration, and performance tests
• 📈 Visualization: Advanced metrics analysis and plotting capabilities
• 🔒 Security Ready: Designed for future integration with CKKS encryption
• 🚀 CI/CD: Automated testing and quality assurance via GitHub Actions
• 📚 Documentation: Comprehensive guides and API documentation

🏗️ Architecture Overview

fl_simulation/
├── 📁 common/                 # Core utilities and schemas
│   ├── schemas.py            # Data structures and validation
│   └── efficiency_metrics.py # FL performance analysis
├── 📁 simulation/            # FL simulation engine
│   └── client_simulation.py  # Main simulation logic
├── 📁 data/                  # Data generation and storage
│   ├── clients/              # Client datasets
│   └── simulate_health_data.py
├── 📁 updates/               # Model updates storage
│   ├── json/                 # Human-readable updates
│   └── numpy/                # Binary updates for processing
├── 📁 visualize/             # Analysis and visualization
│   └── metrics_analysis.py   # Metrics plotting and analysis
├── 📁 tests/                 # Comprehensive testing suite
│   ├── test_efficiency_metrics.py
│   ├── test_client_simulation.py
│   └── run_tests.py
├── 📁 scripts/               # Automation and utilities
│   └── run_experiments.py    # Multi-experiment runner
└── 📁 .github/workflows/     # CI/CD automation

🚀 Quick Start

1. Installation

# Clone the repository
git clone <your-repo-url>
cd fl_simulation

# Install dependencies
pip install -r requirements.txt

# Verify installation
python3 tests/run_tests.py --check-deps

2. Run Basic Simulation

# Run FL simulation with 5 clients, 3 rounds
python3 simulation/client_simulation.py

# Expected output:
# Starting Federated Learning Simulation...
# Loaded data for 5 clients
# Initialized global model with 4 features
# --- Round 1/3 ---
#   Training client client_0 with 200 samples...
#   ...
# Simulation completed successfully!

3. Analyze Results

# Generate efficiency metrics and visualizations
python3 visualize/metrics_analysis.py

# View generated plots in metrics/ directory
open metrics/accuracy_analysis.png
open metrics/efficiency_metrics.png

📊 Efficiency Metrics

Our framework provides comprehensive FL efficiency analysis:

Communication Efficiency

• Total communication rounds
• Bytes transferred
• Communication overhead percentage

Training Efficiency

• Training time per round
• Convergence analysis
• Resource utilization

Model Performance

• Accuracy improvement tracking
• Weight convergence analysis
• Loss reduction metrics

Resource Metrics

• Memory usage optimization
• CPU utilization tracking
• Scalability analysis

🧪 Testing Framework

Test Coverage: 80%+ Target

# Run all tests
python3 tests/run_tests.py

# Run with coverage reporting
python3 tests/run_tests.py --coverage

# Run specific test modules
python3 tests/run_tests.py --module test_efficiency_metrics

# Performance testing
python3 tests/run_tests.py --performance

Test Categories

• Unit Tests: Individual component validation
• Integration Tests: Component interaction testing
• Performance Tests: Speed and efficiency benchmarks
• Edge Case Tests: Error handling and boundary conditions

Automated Quality Assurance

• GitHub Actions: Automated testing on every push/PR
• Code Coverage: Track test coverage over time
• Linting: Code quality and style enforcement
• Security: Vulnerability scanning and dependency checks

🔬 Advanced Usage

Multi-Experiment Runner

# Run multiple experiments with different configurations
python3 scripts/run_experiments.py

# This will run simulations with:
# - 2 rounds, 3 rounds, 4 rounds, 5 rounds
# - Generate comprehensive metrics for comparison
# - Save results for analysis

Custom Configurations

# Modify simulation parameters in simulation/client_simulation.py
NUM_ROUNDS = 5          # Number of FL rounds
DATA_DIR = "data/clients"  # Client data directory
OUTPUT_JSON = "updates/json"  # JSON output location
OUTPUT_NPY = "updates/numpy"  # NumPy output location

Future Integration Ready

The framework is designed to be easily extended for future collaborative work:

# Updates are saved in standardized formats:
# JSON: Human-readable for debugging
# NumPy: Binary format for encryption processing

# Example update structure:
{
    "client_id": "client_0",
    "round_id": 0,
    "weight_delta": [0.1, -0.2, 0.3, -0.1],
    "bias_delta": 0.05,
    "num_samples": 200
}

📈 Results and Analysis

Generated Files

After running simulations, you'll find:

metrics/
├── fl_simulation_3rounds_5clients.json  # Individual experiment
├── metrics_summary.json                  # Aggregated results
├── metrics_history.csv                   # CSV for analysis
├── accuracy_analysis.png                 # Accuracy plots
├── efficiency_metrics.png                # Efficiency plots
├── convergence_trends.png                # Convergence analysis
└── analysis_report.json                  # Detailed report

Key Metrics Output

==================================================
FL EFFICIENCY METRICS SUMMARY
==================================================
Communication Rounds: 15
Bytes Transferred: 15.00 KB
Final Accuracy: 0.7980
Accuracy Improvement: 0.2980
Convergence Round: Not reached
Memory Usage: 0.0000 MB
==================================================

🛠️ Development

Adding New Features

1. Create Tests First: Follow TDD principles
2. Update Documentation: Keep README current
3. Run Quality Checks: Ensure all tests pass
4. Update Requirements: Add new dependencies

Code Quality Standards

• Test Coverage: Maintain 80%+ coverage
• Linting: Follow PEP 8 standards
• Type Hints: Use type annotations
• Documentation: Comprehensive docstrings

Testing Best Practices

# Before committing:
python3 tests/run_tests.py --coverage
python3 -m flake8 common simulation visualize
python3 -m black --check common simulation visualize

🔧 Configuration

Environment Variables

# Optional: Set custom paths
export FL_DATA_DIR="/path/to/data"
export FL_OUTPUT_DIR="/path/to/output"
export FL_LOG_LEVEL="INFO"

Performance Tuning

# In simulation/client_simulation.py
global_model = LogisticRegression(
    penalty=None,
    fit_intercept=True,
    solver="lbfgs",
    max_iter=1000,        # Increase for better convergence
    warm_start=True,
    random_state=42       # For reproducibility
)

📚 API Reference

Core Classes

FLEfficiencyMetrics

@dataclass
class FLEfficiencyMetrics:
    timestamp: str
    num_clients: int
    num_rounds: int
    total_samples: int
    # ... and many more metrics

FLEfficiencyCalculator

class FLEfficiencyCalculator:
    def calculate_efficiency_metrics(self, clients_data, global_model, num_rounds, training_time=None)
    def save_metrics(self, metrics, experiment_name=None)
    def calculate_communication_efficiency(self, num_clients, num_rounds)

Main Simulation Functions

def main():
    """Main FL simulation workflow"""
    # Load client data
    # Initialize global model
    # Run FL rounds
    # Calculate efficiency metrics
    # Save results

🚨 Troubleshooting

Common Issues

1. Import Errors: Ensure Python path includes project root
2. Missing Dependencies: Run pip install -r requirements.txt
3. Test Failures: Check test output for specific error details
4. Performance Issues: Verify system resources and data sizes

Debug Mode

# Run with maximum verbosity
python3 tests/run_tests.py --verbosity 2

# Single test debugging
python3 -m pytest tests/test_efficiency_metrics.py::TestFLEfficiencyMetrics::test_metrics_creation -s

Getting Help

1. Check this README
2. Review test output carefully
3. Check dependency versions
4. Verify test environment
5. Create issue with detailed error information

🔮 Future Enhancements

Planned Features

• Distributed Training: Multi-node FL simulation
• Advanced Aggregation: FedProx, FedNova algorithms
• Real-time Monitoring: Live metrics dashboard
• Benchmarking Suite: Compare different FL approaches
• Export Formats: TensorFlow, PyTorch model export

Research Integration

• Paper Reproduction: Standard FL algorithm implementations
• Custom Algorithms: Easy integration of new FL methods
• Performance Analysis: Comprehensive benchmarking tools
• Publication Ready: Generate publication-quality plots

Collaborative Extensions

The framework is designed to be easily extended for collaborative research:

• Encryption Layer: Integration with CKKS homomorphic encryption
• Advanced Aggregation: Custom aggregation algorithms
• Multi-Party Computation: Secure multi-party FL protocols
• Blockchain Integration: Decentralized FL coordination

🤝 Contributing

We welcome contributions! Please follow our development workflow:

1. Fork the repository
2. Create a feature branch
3. Add comprehensive tests
4. Ensure all tests pass
5. Update documentation
6. Submit a pull request

Development Setup

# Install development dependencies
pip install -r requirements.txt

# Run quality checks
python3 tests/run_tests.py --coverage
python3 -m flake8 common simulation visualize
python3 -m black common simulation visualize
python3 -m isort common simulation visualize

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Research Community: FL algorithm implementations and research papers
• Open Source: Testing and development tools
• Academic Institutions: Supporting federated learning research
• Industry Partners: Real-world FL applications and use cases

For detailed author information and research background, see AUTHOR.md.

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Ready to revolutionize your federated learning research? 🚀

Start with python3 simulation/client_simulation.py and explore the comprehensive testing framework with python3 tests/run_tests.py --coverage!