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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This post provides an overview of four current research projects at Scaleout. Each project explores advanced AI methods to tackle real-world challenges in satellite data processing, autonomous vehicles, and fleet intelligence. By focusing on privacy, efficiency, and adaptability, these initiatives demonstrate our ongoing commitment to developing useful and reliable machine learning solutions for complex environments.
The post introduces a toolkit for large-scale federated learning that successfully simulated 10,000 asynchronous, intermittently connected clients. It achieved stable model convergence while keeping data private on devices, demonstrating fault tolerance, scalability, and efficiency for privacy-preserving AI at scale.
The article introduces an edge-based data selection pipeline for federated machine learning, designed to improve model training without sending raw video streams to central servers. This preserves privacy, reduces bandwidth and latency, and ensures that only the most valuable data contributes to global model updates.
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