What is federated learning?

One of the key challenges of machine learning is the need for large amounts of data. Gathering training datasets for machine learning models poses privacy, security, and processing risks that organizations would rather avoid. One technique that can help address some of these challenges is "federated learning." By distributing the training of models across user devices, federated learning makes it possible to take advantage of machine learning while minimizing the need to collect user data. The traditional process for developing machine learning applications is to gather a large dataset, train a model on the data, and run the trained model on a cloud server that users can reach through different applications such as web search, translation, text generation, and image processing.