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NEBULA

A Platform for Decentralized Federated Learning

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🌌 About NEBULA#

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.

🚀 Key Components#

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#

  • 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.

  • Real-time monitoring: Provides live performance metrics and visualizations during the learning process.

🌍 Scenario Applications#

  • Healthcare: Train models on medical devices such as wearables, smartphones, and sensors.

  • Industry 4.0: Implement on industrial devices like robots, drones, and constrained devices.

  • Mobile services: Optimize for mobile devices including smartphones, tablets, and laptops.

  • Military: Apply to military equipment such as drones, robots, and sensors.

  • Vehicular scenarios: Utilize in vehicles including cars, trucks, and drones.

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

For any questions, please contact Enrique Tomás Martínez Beltrán (enriquetomas@um.es).


University of Murcia logo Armasuisse logo University of Zurich logo



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