Discover how federated learning enables secure AI applications across automotive, defence, and cybersecurity while preserving data privacy.
This video explains federated machine learning with a simple example for non-technical audiences.
It emphasizes privacy preservation as a main benefit and explores other advantages for various industries.
It discusses the need for federated learning, its basic mechanics, and additional benefits beyond privacy.
We are happy to serve a diverse range of federated learning use cases. This section highlights a few of the organizations we've had the privilege of working with.
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Scaleout's FEDAIR project for NATO's DIANA programme enables secure, decentralized ML model updates in conflict zones, allowing adaptation without compromising sensitive data.
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A fully autonomous drone tested in Arctic Sweden uses onboard AI to detect, track, and pursue targets without GPS or connectivity, proving reliable edge intelligence for defense in communication-denied environments.
Akkodis and Scaleout are partnering to combine rugged industrial hardware with federated learning capabilities to accelerate the deployment of secure, scalable Edge AI solutions in mission-critical sectors like defense, energy, and transportation.
Federated Learning enables collaboration without sharing medical data, but inconsistent annotations limit its impact. Self-Supervised Learning solves this by leveraging unannotated images to pre-train models, then fine-tuning on small labeled sets. A study on LUND-PROBE showed SSL models outperforming U-Net with minimal data, proving that combining FL and SSL can unlock siloed medical datasets and reduce annotation needs.
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