NEBULA is a cutting-edge platform facilitating the training of federated models across both centralized and decentralized architectures. It streamlines the development, deployment, and management of federated applications on physical and virtualized devices.

Core Architecture

• Frontend: An intuitive user interface for configuring experiments and monitoring their progress.
• Controller: An orchestrator ensuring efficient management of operations.
• Core: The fundamental component deployed on each device to execute federated learning processes.

Key Features

• Decentralization: Train models without a central server, leveraging decentralized federated learning.
• Privacy-Preserving: Maintain data privacy by training on-device and sharing only model updates.
• Trustworthiness: Ensure the integrity of the learning process by verifying federation reliability.
• Blockchain Integration: Supports blockchain technologies for enhanced security and transparency.
• Security: Robust security mechanisms to protect data and models.
• Topology-Agnostic: Supports various network topologies, including star, ring, and mesh.
• Model-Agnostic: Compatible with a wide range of machine learning algorithms, from deep learning to traditional methods.
• Network Communication: Secure and efficient device communication with features like compression, network failure tolerance, and condition simulation.
• Real-Time Monitoring: Provides live performance metrics and visualizations during the learning process.

NEBULA is developed by Enrique Tomás Martínez Beltrán in collaboration with the University of Murcia, Armasuisse, and the University of Zurich.

For the complete list of contributors, please visit the contributors page.