Conventional machine learning struggles at the edge, where cloud-trained models can't effectively handle distributed data sources, meet real-time requirements, or address privacy constraints.
Edge-to-cloud deployments demand a fundamentally different approach.
Data Bottlenecks: Moving massive edge data to central servers creates bandwidth costs and network congestion
Performance Gaps: Cloud-based models create unacceptable latency and don't function offline or adapt to local conditions
Control Limitations: Organizations lose ownership of their data, limiting governance and cross-organizational collaboration, while also raising privacy concerns
Integration Complexity: Managing AI across the entire edge-to-cloud continuum creates technical barriers that slow innovation and fragment your ML operations
Purpose-built federated learning solutions for sectors with unique edge computing challenges
We support diverse federated learning initiatives with partners including SAAB, the Swedish Defence Materiel Administration (FMV), NATO DIANA, Scania, Eurocontrol, and other leading organizations.
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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