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1. (WO2018057302) COMMUNICATION EFFICIENT FEDERATED LEARNING

Pub. No.:    WO/2018/057302    International Application No.:    PCT/US2017/050433
Publication Date: Fri Mar 30 01:59:59 CEST 2018 International Filing Date: Fri Sep 08 01:59:59 CEST 2017
IPC: G06N 99/00
Applicants: GOOGLE LLC
Inventors: MCMAHAN, Hugh Brendan
BACON, David Morris
KONECNY, Jakub
YU, Xinnan
Title: COMMUNICATION EFFICIENT FEDERATED LEARNING
Abstract:
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.