Federated learning with partially trainable networks

A server computing system, comprising:

• one or more processors;
• and
• one or more non-transitory computer-readable media that collectively store:
• a machine learning model having a plurality of global parameters;
• and
• instructions that, when executed by the one or more processors, cause the server computing device to perform operations, the server operations comprising:
• determining, by a server computing device, a first set of training parameters from a plurality of parameters of the global model, wherein the plurality of parameters of the global model includes the first set of training parameters and a set of frozen parameters;
• transmitting, respectively to a plurality of client computing devices, the first set of training parameters and an initialization value, wherein the set of frozen parameters are reconstructed from the initialization value by the plurality of client computing devices;
• receiving, respectively from the plurality of client computing devices, updates to one or more parameters in the first set of training parameters, wherein the updates to one or more parameters were generated respectively by the plurality of computing devices using a local model stored respectively in the plurality of client computing devices;
• aggregating the updates to one or more parameters that are respectively received from the plurality of client computing devices;
• and
• modifying one or more global parameters of the machine learning model based on the aggregation of the updates to the one or more parameters that are respectively received from the plurality of client computing devices.

One or more non-transitory computer-readable media that collectively store a machine learning model having been updated by performance of operations, the operations comprising:

• determining a first set of training parameters from a plurality of parameters of the global model, wherein the plurality of parameters of the global model includes the first set of training parameters and a set of frozen parameters;
• transmitting, respectively to a plurality of client computing devices, the first set of training parameters and an initialization value, wherein the set of frozen parameters are reconstructed from the initialization value by the plurality of client computing devices;
• receiving, respectively from the plurality of client computing devices, updates to one or more parameters in the first set of training parameters, wherein the updates to one or more parameters were generated respectively by the plurality of computing devices using a local model stored respectively in the plurality of client computing devices;
• aggregating the updates to one or more parameters that are respectively received from the plurality of client computing devices;
• and
• modifying one or more global parameters of the machine learning model based on the aggregation of the updates to the one or more parameters that are respectively received from the plurality of client computing devices.