Federated learning

Federated learning (also known as collaborative learning) is a machine learning technique in a setting where multiple entities (often called clients) collaboratively train a model while keeping their data decentralized, rather than centrally stored. A defining characteristic of federated learning is data heterogeneity. Because client data is decentralized, data samples held by each client may not be independently and identically distributed.

Federated learning - Wikipedia Jump to content From Wikipedia, the free encyclopedia Decentralized machine learning Diagram of a Federated Learning protocol with smartphones training a global AI model Federated learning (also known as collaborative learning ) is a machine learning technique in a setting where multiple entities (often called clients) collaboratively train a model while keeping their data decentralized , [ 1 ] rather than centrally stored. A defining characteristic of federated learning is data heterogeneity . Because client data is decentralized, data samples held by each clien

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