Generating model update data at satellite
AssigneeMicrosoft Technology Licensing, LLC
InventorsTsu-wang HSIEH, Jin Hyun SO, Behnaz ARZANI, Shadi ABDOLLAHIAN NOGHABI, Ranveer CHANDRA
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Abstract
A satellite is provided, including an onboard computing device. The onboard computing device may include a processor configured to receive training data while the satellite is in orbit. The processor may be further configured to perform training at a machine learning model based at least in part on the training data. The processor may be further configured to generate model update data that specifies a modification made to the machine learning model during the training. The processor may be further configured to transmit the model update data from the satellite to an additional computing device.
Claims20
A satellite comprising:
- an onboard computing device including a processor configured to:
- receive training data while the satellite is in orbit;
- perform training of a neural network based at least in part on the training data to thereby generate a modified neural network with modified parameters;
- generate neural network update data;
- and
- transmit the neural network update data from the satellite to an additional computing device at a time specified by an uplink-downlink schedule stored in memory at the satellite.
A computing device comprising:
- a processor configured to:
- generate a respective aggregation schedule for each of a plurality of satellites, wherein the aggregation schedule generated for a satellite specifies a local neural network version difference interval for the satellite;
- for each satellite of the plurality of satellites, generate an uplink-downlink schedule for the satellite based at least in part on the respective aggregation schedule for that satellite, wherein the uplink-downlink schedules each include a plurality of training iterations performed at the corresponding satellite;
- receive aggregation data from the plurality of satellites by communicating with the satellites according to the uplink-downlink schedules, wherein the aggregation data includes neural network update data that specifies, for each satellite of the plurality of satellites, a corresponding modification made to a respective neural network during training of the neural network at the satellite;
- and
- perform training at an aggregated neural network based at least in part on the aggregation data received from the plurality of satellites, wherein, for each satellite of the plurality of satellites, the training is performed at the local neural network version difference interval specified for that satellite in the aggregation schedule.
A method for use with a computing device, the method comprising:
- generating a respective aggregation schedule for each of a plurality of satellites, wherein the aggregation schedule generated for a satellite specifies a local neural network version difference interval for the satellite;
- for each satellite of the plurality of satellites, generating an uplink-downlink schedule for the satellite based at least in part on the respective aggregation schedule for that satellite, wherein the uplink-downlink schedules each include a plurality of training iterations performed at the corresponding satellite;
- receiving aggregation data from the plurality of satellites by communicating with the satellites according to the uplink-downlink schedules, wherein the aggregation data includes neural network update data that specifies, for each satellite of the plurality of satellites, a corresponding modification made to a respective neural network during training of the neural network at the satellite;
- and
- performing training at an aggregated neural network based at least in part on the aggregation data received from the plurality of satellites, wherein, for each satellite of the plurality of satellites, the training is performed at the local neural network version difference interval specified for that satellite in the aggregation schedule.
Dependencies are inferred from the claim text.