Federated learning

Federated learning (also known as collaborative learning) is a sub-field of machine learning focusing on settings in which multiple entities (often referred to as clients) collaboratively train a model while ensuring that their data remains decentralized. This stands in contrast to machine learning settings in which data is centrally stored. One of the primary defining characteristics of federated learning is data heterogeneity. Due to the decentralized nature of the clients' data, there is no guarantee that data samples held by each client are independently and identically distributed.

Federated learning is generally concerned with and motivated by issues such as data privacy, data minimization, and data access rights. Its applications involve a variety of research areas including defense, telecommunications, the Internet of Things, and pharmaceuticals.