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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Scaleout Systems secured 35 MSEK with Fairpoint Capital joining existing investors to enhance its FEDn framework for secure cloud-edge AI deployment. The funding will help address data processing challenges in industrial IoT, automotive and defense sectors while maintaining data sovereignty.
Federated learning represents a shift in AI architecture where models travel to and train on distributed data sources, eliminating the need to centralize sensitive data. This approach enables organizations and devices to learn collaboratively while maintaining data privacy and reducing latency for real-time edge processing.
The future of AI is in the cloud-edge continuum, allowing organizations to use both centralized machine learning and edge computing. As data increases, federated learning becomes important, letting models learn from local data while keeping privacy and optimizing resources across devices.
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