Federated Learning

Discover the power of decentralization for secure analysis while retaining data locality

Duality enables secure collaboration using FL & Secure FL

Federated Learning (FL) is a distributed machine learning (ML) technique that enables model training on data from multiple, decentralized servers with local data samples, without exchanging or moving data. This approach ensures that the data remains in its original location and is not exposed to any other parties.
Another characteristic of FL is that it is typically composed of heterogeneous datasets where the size of the dataset varies from one data owner to another.
Duality developed a second layer of security on top of FL to ensure that the federated computation itself is performed while encrypted, keeping the model concealed from all participants and the computation operation secured.


Use cases

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