Solving the data access challenge in AI
FEDn is our federated learning solution to make it possible to train machine learning models on distributed data. The data can stay where it is and doesn't have to be moved or disclosed.

This approach enables several organizations to collaborate on machine learning models, but without needing to directly share sensitive or confidential data with each other.
Andreas Hellander, CSO Scaleout
Federated learning with FEDn
An introduction to the three key challenges of Federated Learning faces as a technology - data heterogeneity, system heterogeneity and scalability. And how we approach some of these challenges at Scaleout with the product FEDn.
Introduction to federated learning
Federated Machine Learning (FedML) is a distributed machine learning approach which enables training on decentralised data. A server coordinates a network of nodes, each of which has local, private training data.

The nodes contribute to the construction of a global model by training on local data , and the server combines non-sensitive node model contributions into the global model.
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