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Not a single line of this project was written by a human! The entire codebase, including this README, was generated by the Memex coding agent. From system architecture to implementation, from visualization to documentation - it's all AI-powered! 🚀

A complete federated learning system implemented in Rust with real-time visualization and monitoring capabilities. The system demonstrates the practical implementation of federated learning principles, where multiple workers train on their local data while contributing to a global model.

• Coordinator: Central server managing the federated learning process
  • Global model aggregation
  • Worker registration and management
  • Training coordination
• Workers: Distributed training nodes
  • Local model training
  • Parameter updates
  • Model synchronization
• Model: Linear model implementation with serialization support
  • Parameter management
  • Forward pass computation
  • Federated averaging implementation

• Real-time Dashboard: Built with Streamlit
  • Training progress visualization
  • Worker performance monitoring
  • Convergence analysis
  • System metrics
• Worker Simulation: Testing and demonstration
  • Simulated distributed training
  • Configurable worker behavior
  • Real-time updates

• Rust 2021 edition
• Dependencies:
  • tokio (async runtime)
  • axum (web framework)
  • ndarray (numerical computations)
  • serde (serialization)

• Python 3.9+
• Dependencies:
  • streamlit
  • plotly
  • pandas
  • numpy

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

# Build Rust components
cargo build

# Set up Python environment
python -m venv .venv
source .venv/bin/activate  # On Windows, use `.venv\Scripts\activate`
pip install -r dashboard/requirements.txt

1. Start the Rust server:

cargo run

2. Launch the Streamlit dashboard:

streamlit run dashboard/app.py

3. Run worker simulation (optional):

python dashboard/simulate_workers.py

.
├── src/
│   ├── main.rs           # Server implementation
│   ├── coordinator.rs    # Federated learning coordinator
│   ├── worker.rs         # Worker implementation
│   └── model.rs          # Linear model and federated averaging
├── dashboard/
│   ├── app.py           # Streamlit dashboard
│   └── simulate_workers.py # Worker simulation
└── README.md

• Asynchronous Communication: Built on tokio for efficient async operations
• Real-time Monitoring: Live training progress and performance metrics
• Federated Averaging: Efficient model parameter aggregation
• Interactive Dashboard: Rich visualizations and system insights
• Scalable Architecture: Support for multiple concurrent workers
• Robust Error Handling: Graceful handling of worker failures and edge cases

MIT License

Contributions are welcome! Please feel free to submit a Pull Request.