Federated AI with FEDn on Safespring

Cloud-based Integration

Collaborative Environment

Data Privacy and Security

Federated Learning

Advanced Machine Learning

Safespring Infrastructure

Scale Securely with FEDn on Safespring

Unlock the power of federated learning to develop robust AI models without compromising data privacy. Scale seamlessly with FEDn, powered securely by Safespring's infrastructure.

FEDn is an enterprise-ready, open-source federated learning framework from Scaleout Systems, designed to allow collaborative machine learning training while fully respecting data privacy and GDPR compliance. Safespring provides the secure, resilient infrastructure necessary for deploying and scaling FEDn, ensuring your AI initiatives remain compliant, secure, and efficient.

By leveraging Safespring’s secure cloud platform combined with FEDn’s powerful federated learning capabilities, your organization can:

Maintain data privacy with local data training.
Scale from pilot projects to full production seamlessly.
Ensure full GDPR and data protection compliance.
Easily integrate with existing ML workflows.

Ebba from Scaleout shares insights on the importance of managing sensitive data

“Since machine learning involves sensitive data, secure management and relying on trusted infrastructure like Safespring's becomes critical.”

Read Use Case

Frequently Asked Questions (FAQ)

What is federated learning, and why is it important?

Federated learning enables machine learning models to train on decentralized datasets without transferring raw data, ensuring privacy and compliance, crucial for sensitive sectors like healthcare, finance, and public administration.

How does FEDn ensure GDPR compliance?

FEDn allows models to be trained locally on client-side data, sending only encrypted model parameters back to the central aggregator. This prevents raw data transfer, ensuring full GDPR compliance.

Can FEDn integrate with existing AI frameworks?

Yes, FEDn supports popular machine learning frameworks including TensorFlow, PyTorch, Keras, Hugging Face, and scikit-learn, enabling seamless integration into your existing AI development processes.

Distributed & Scalable
FEDn's hierarchical design with multiple aggregation servers ensures efficient workload distribution and scalability, supporting thousands of clients seamlessly.

Real-Time Monitoring & Analytics
Benefit from built-in real-time monitoring, analytics, and system recovery, enhancing operational reliability and visibility into your federated experiments.

Seamless Integration
Easily integrate FEDn with popular AI frameworks like TensorFlow and PyTorch, and deploy flexibly across Safespring's secure cloud infrastructure.