Use cases
From enhancing cybersecurity and defense systems to improving vehicle safety through real-time, on-the-edge processing, federated learning ensures data privacy and strengthens AI security. Learn more »
Dual-use AI
From R&D to real-world federated learning
The FEDn framework enables seamless development and deployment of federated learning (FL) applications. It is designed to support the entire R&D innovation journey, from personal testing and proof-of-concepts to real-world, geographically distributed FL. FEDn is scalable, resilient, secure, and cloud-native, offering flexible deployment options while supporting all major machine learning (ML) frameworks.
What is federated learning?
Federated learning (FL) overcomes the limitations of centralized machine learning by training models on data spread across different locations, preserving privacy and complying with regulations. This decentralized approach enables secure, efficient, and scalable machine learning without moving the data, useful for managing the growing complexity and volume of data in a connected world.
The FEDn framework
The FEDn framework enables the seamless development and deployment of federated learning applications, supporting projects from initial local proofs-of-concept to fully distributed real-world implementations. You can start your Federated Machine Learning (FML) project in a simulated local environment and then easily transition it to FEDn Studio for actual deployment, all without needing to modify your existing code.
Dual-use AI
Workshop
Join one of our online workshops to see federated learning in practice and talk directly to the team.
FAQ
Visit the section with frequently asked questions collected on the framework page.
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