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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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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