Modern mobile devices have access to a wealth of data suitable for learning models, which is often privacy sensitive. This may preclude logging to the data center and training using conventional approaches. For this reason, here is proposed an alternative that leaves the training data distributed on the mobile devices, and learns a shared model...
Federated learning is a potential solution for developing machine-learning models requirining massive datasets. However, it may have positive impacts on data protection such as avoiding data centralisation and simplifying control over data processing. Learn more on federate learning with the EDPS here
Synthetic data is artificial data able to reproduce information very close to the original data. This means they can maintain almost the same statistical value as the authentic ones. Sounds interesting? Learn more about them with the EDPS here
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