🌐 Federated Sensor Network (FedSense)

A framework for privacy-preserving federated learning in sensor networks. This project demonstrates advanced concepts in pattern recognition, privacy preservation, and distributed learning systems.

🌟 Key Features

Pattern Recognition

• Dynamic Pattern Detection: Real-time analysis of sensor readings
• Event Impact Analysis: Intelligent event-based pattern recognition
• Adaptive Learning: Continuous pattern refinement and accuracy improvement

Privacy Preservation

• Privacy Metrics: Real-time privacy score tracking
• Data Protection: Minimal raw data sharing
• Pattern Anonymization: Secure pattern sharing mechanisms

Advanced Analytics

• Real-time Visualization: Dynamic multi-plot analysis
• Pattern Library: Centralized pattern knowledge base
• Performance Metrics: Comprehensive accuracy tracking

🚀 Quick Start

Installation

cd federated-sensor-network

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

# Install the package
pip install -e .

Basic Usage

from fedsense.network import EnhancedFederatedNetwork

# Create and run a simulation
network = EnhancedFederatedNetwork()
network.run()

🎮 Advanced Usage

Custom Sensor Configuration

from fedsense.core import EnhancedSensor
import numpy as np

# Initialize custom sensor
sensor = EnhancedSensor(
    name="Custom Sensor",
    location=(0, 0),
    pattern_type="factory"
)

# Configure learning parameters
sensor.base_temp = 25.0
sensor.add_known_event("custom_event", 
    start_hour=10, 
    duration=2
)

Pattern Analysis

# Analyze patterns with custom window
patterns = sensor.learn_patterns(window_size=48)

# Access specific pattern metrics
daily_range = patterns['daily_range']
variance = patterns['variance']
trend = patterns['trend']
peak_hours = patterns['peak_hours']

🔧 Technical Details

Core Components

1. Sensor Systems

   • Temperature pattern generation
   • Event impact modeling
   • Local pattern learning

2. Privacy Framework

   • Privacy score calculation
   • Data sharing controls
   • Pattern anonymization

3. Federated Learning

   • Distributed pattern recognition
   • Global knowledge aggregation
   • Model synchronization

Performance Features

• Real-time pattern analysis
• Dynamic visualization updates
• Privacy-preserving data sharing
• Neural network-based learning

📊 Applications

• Industrial IoT: Factory sensor networks
• Smart Buildings: HVAC optimization
• Environmental Monitoring: Weather pattern analysis
• Privacy Research: Data protection studies
• Distributed Systems: Federated learning research

📄 License

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

🙏 Acknowledgments

• Neural network components powered by PyTorch
• Visualization built on Matplotlib
• Console interface using Rich
• Special thanks to all contributors and the open-source community

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