Google Inc.

University of Washington

Dave Morris Bacon

Hugh Brendan McMahan

Xinnan Yu

Jakub Konecny

Gregory M Crosswhite

Steven T Flammia

The present disclosure provides efficient communication techniques for transmission of model updates within a machine learning framework, such as, for example, a federated learning framework in which a high-quality centralized model is trained on training data distributed overt a large number of clients each with unreliable network connections and low computational power. In an example federated learning setting, in each of a plurality of rounds, each client independently updates the model based on its local data and communicates the updated model back to the server, where all the client-side updates are used to update a global model. The present disclosure provides systems and methods that reduce communication costs. In particular, the present disclosure provides at least: structured update approaches in which the model update is restricted to be small and sketched update approaches in which the model update is compressed before sending to the server.

Communication efficient federated learning

A quantum computational (QC) device includes a multi-qubit (MQ) structure and another MQ structure coupled to the MQ structure. The MQ structure is arranged to provide an adiabatic quantum computation by application of an initial Hamiltonian operator. The other MQ structure is arranged to provide another adiabatic quantum computation by application of another Hamiltonian operator that is spatially different than the initial Hamiltonian operator.

Quantum computational device employing multi-qubit structures and associated systems and methods

Growing community of inventors

Seattle, WA, United States of America

Dave Morris Bacon