Introduction

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

NEBULA boasts a modular architecture that consists of three core elements:

• Frontend: A user-friendly interface for setting up experiments and monitoring progress.
• Controller: An orchestrator that ensures efficient operation management.
• Core: The fundamental component deployed on each device to handle federated learning processes.

Main features of the platform include:

• Decentralized: Train models without a central server, leveraging decentralized federated learning.
• Privacy-preserving: Maintain data privacy by training on-device and only sharing model updates.
• Topology-agnostic: Support for 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.
• Trustworthiness: Ensure the integrity of the learning process by verifying the reliability of the federation.
• Blockchain integration: Support for blockchain technologies to enhance security and transparency.
• Security: Robust security mechanisms to protect data and models.
• 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.