Federated learning and machine learning are related, but distinct, concepts. Machine learning refers to the development of algorithms and statistical models that enable computers to improve their ...

Federated learning lets a network of participants collaboratively train algorithms on data while keeping each stakeholder's data in its home location.

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

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

Banks/Fintechs can utilize Federated Learning to provide tailored finance offers (for example, credit cards or investments) ...

IBM’s Federated Learning Framework IBM FL is built with a Python library designed to support the machine learning process in a distributed environment.

Federated learning can elevate AI. By securing model training, it unlocks a myriad of use cases that can change the world as we know it.

Currently, there is a slight additional computational cost for developing federated learning models as well as a limitation to neural networks as the main supported algorithm by the most common ...