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.
%201%20(1).webp)
Scaleout's FEDAIR project for NATO's DIANA programme enables secure, decentralized ML model updates in conflict zones, allowing adaptation without compromising sensitive data.
Read more »
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.
%201.svg)
