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 »
Federated learning allows hospitals to collaboratively train AI models for tasks like lung cancer detection without sharing sensitive patient data.
We are launching a two-year project to develop a resource-efficient, privacy-preserving AI framework for connected fleets by combining Federated Learning and Mixture-of-Experts models, enabling context-aware, scalable, and accurate decision-making at the edge.
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.
%201.svg)
