These code snippets are based on the Asynchronous Federated Learning Tutorial from the OpenMined github repository which trains a model on MNIST handwritten digit recognition data.

Federated Learning solves two big problems of data analysis: improved qualitative analyses for society and safeguarding of one's privacy.

Federated learning is essentially machine learning for inaccessible data—the data could be private, or the data owner may not want to lose ownership.

By distributing the training of models across devices, federated learning ensures use of machine learning while minimizing data collection.

The author of a study on clinical federated learning walks readers through various forms of artificial intelligence and shows how provider organizations can use each.

As enterprises struggle to balance AI capabilities against data privacy concerns, federated learning provides the best of both worlds.

Federated learning processes were applied to both artificial neural networks (ANNs) and logistic regression (LR) models on the horizontal data sets that are varying in count and availability.

Federated learning’s popularity is rapidly increasing because it addresses common development-related security concerns.