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DISTRIBUTED MODEL TRAINING

  • US 20150193695A1
  • Filed: 01/27/2014
  • Published: 07/09/2015
  • Est. Priority Date: 01/06/2014
  • Status: Active Grant
First Claim
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1. A method comprising:

  • determining, by a device, that a machine learning model is to be trained by a plurality of devices in a network;

    identifying a set of training devices from among the plurality of devices in the network to train the machine learning model, wherein each of the training devices has a local set of training data;

    sending an instruction to each of the training devices, wherein the instruction is configured to cause a training device to receive model parameters from a first training device in the set, use the received model parameters with at least a portion of the local set of training data to generate new model parameters, and forward the new model parameters to a second training device in the set; and

    receiving model parameters from the training devices that have been trained using a global set of training data comprising the local sets of training data on the training devices.

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