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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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.
AI everywhere requires low latency, bandwidth efficiency, and privacy. Federated Learning enables this by training models on-device, sharing only updates. This makes Edge AI secure, efficient, and adaptive, forming the foundation for scalable, real-world AI deployment.
Federated Learning enhances privacy but is vulnerable to data and model poisoning attacks. Tests with our new simulator show adaptive strategies like EE-Trimmed Mean are more resilient than traditional methods.
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