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 article introduces Vertical Federated Learning (Vertical FL) in FEDn, which allows organizations with complementary data features on the same individuals to train a shared model collaboratively without sharing private data, improving accuracy and preserving privacy, as shown in a diabetes prediction example.
Python vs C++ clients in FEDn: Python uses less idle memory but C++ is more efficient during training with better memory management and faster task completion. FEDn supports both simultaneously.
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.
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