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A Scalable Approach for Partially Local Federated Learning
Posted by Karan Singhal, Senior Software Engineer, Google Research Federated learning enables users to train a model without sending raw data to a central server, thus avoiding the collection of privacy-sensitive data. Often this is done by learning a single global model for all users, even though the users may differ in their data distributions. For example, users of a mobile keyboard application may collaborate to train a suggestion model but have different preferences for the suggestions. Th...