Federated learning platform and methods for using same

A computing system for federated machine learning comprising:

• a central system communicatively coupled to a plurality of satellite site systems;
• and
• one or more processors coupled to one or more memory devices, wherein the one or more memory devices include instructions which when executed by the one or more processors cause the system to:
• receive a plurality of satellite analytics artifacts from respective satellite site systems of the plurality of satellite site systems;
• generate a central machine learning model based on one or more of the plurality of satellite analytics artifacts, wherein the central machine learning model comprises a set of central values for a set of weights;
• execute a federated machine learning epoch, comprising:
• transmitting the central machine learning model to the plurality of satellite site systems;
• receiving, at the central authority, from each of the respective satellite site systems, a respective set of one or more satellite values for the set of weights, wherein the set of one or more satellite values is generated by a respective satellite site system based on a respective local dataset of the respective satellite site system;
• and
• generating, based on the satellite values received from the satellite site systems, an updated version of the central machine learning model comprising updated central values for the set of weights.

A computer-readable medium that stores instructions that, when executed by a computing device, cause the device to:

• receive a plurality of satellite analytics artifacts from respective satellite site systems of the plurality of satellite site systems;
• generate a central machine learning model based on one or more of the plurality of satellite analytics artifacts, wherein the central machine learning model comprises a set of central values for a set of weights;
• execute a federated machine learning epoch, comprising:
• transmitting the central machine learning model to the plurality of satellite site systems;
• receiving, at the central authority, from each of the respective satellite site systems, a respective set of one or more satellite values for the set of weights, wherein the set of one or more satellite values is generated by a respective satellite site system based on a respective local dataset of the respective satellite site system;
• and
• generating, based on the satellite values received from the satellite site systems, an updated version of the central machine learning model comprising updated central values for the set of weights.

A computer-implemented method for federated machine learning, the method performed by a satellite site system communicatively coupled to a central system communicatively coupled to a plurality of respective satellite site systems, the method comprising:

• generating a set of satellite analytics artifacts based on a local data set stored at the satellite site system;
• transmitting the set of satellite analytics artifacts to the central system;
• receiving, from the central system, a central machine learning model, wherein the central machine learning model is generated based at least in part on the satellite analytics artifacts and wherein the central machine learning model comprises a set of central values for a set of weights;
• generating a set of satellite values for the set of weights, wherein the set of satellite values is generated based on the local data set;
• transmitting the set of satellite values to the central system;
• receiving, from the central system, an updated version of the central machine learning model comprising updated central values for the set of weights, wherein the updated version of the central machine learning model is generated by the central system at least in part based on the set of satellite values.

A computing system for federated machine learning comprising:

• a satellite site system communicatively coupled to a central system communicatively coupled to a plurality of respective satellite site systems;
• and
• one or more processors coupled to one or more memory devices, wherein the one or more memory devices include instructions which when executed by the one or more processors cause the system to:
• generate a set of satellite analytics artifacts based on a local data set stored at the satellite site system;
• transmit the set of satellite analytics artifacts to the central system;
• receive, from the central system, a central machine learning model, wherein the central machine learning model is generated based at least in part on the satellite analytics artifacts and wherein the central machine learning model comprises a set of central values for a set of weights;
• generate a set of satellite values for the set of weights, wherein the set of satellite values is generated based on the local data set;
• transmit the set of satellite values to the central system;
• receive, from the central system, an updated version of the central machine learning model comprising updated central values for the set of weights, wherein the updated version of the central machine learning model is generated by the central system at least in part based on the set of satellite values.

A computer-readable medium that stores instructions that, when executed by a computing device, cause the device to:

• generate a set of satellite analytics artifacts based on a local data set stored at the satellite site system;
• transmit the set of satellite analytics artifacts to the central system;
• receive, from the central system, a central machine learning model, wherein the central machine learning model is generated based at least in part on the satellite analytics artifacts and wherein the central machine learning model comprises a set of central values for a set of weights;
• generate a set of satellite values for the set of weights, wherein the set of satellite values is generated based on the local data set;
• transmit the set of satellite values to the central system;
• receive, from the central system, an updated version of the central machine learning model comprising updated central values for the set of weights, wherein the updated version of the central machine learning model is generated by the central system at least in part based on the set of satellite values.