Systems and Methods for Distributed On-Device Learning with Data-Correlated Availability

A computing device comprising:

• one or more processors;
• and
• one or more non-transitory computer-readable media that store instructions that, when executed by the one or more processors, cause the computing device to:
• generate a plurality of regions based at least in part on temporal availability of user devices;
• select a plurality of available user devices within a region;
• provide a current version of a machine-learned model associated with the region to the plurality of selected user devices within the region, wherein each of the plurality of selected user devices performs training of the current version of the machine-learned model associated with the region using data local to each of the plurality of selected user devices;
• obtain, from the plurality of selected user devices, updated machine-learned model data;
• and
• generate an updated machine-learned model associated with the region based on the updated machine-learned model data.

A system comprising:

• a server;
• and
• a plurality of user devices;
• the server comprising:
• one or more processors;
• and
• one or more non-transitory computer-readable media that store instructions that, when executed by the one or more processors, cause the one or more processors to perform operations, the operations comprising:
• obtaining a plurality of regions based at least in part on temporal availability of user devices;
• selecting, from the plurality of user devices, a plurality of available user devices within a region;
• providing a current version of a machine-learned model associated with the region to the plurality of selected user devices within the region;
• obtaining, from the plurality of selected user devices, updated machine-learned model data;
• and
• generating an updated machine-learned model associated with the region based on the updated machine-learned model data;
• each of the plurality of user devices comprising:
• one or more processors;
• and
• one or more non-transitory computer-readable media that store instructions that, when executed by the one or more processors, cause the one or more processors to perform operations, the operations comprising:
• performing, in response to being selected from the plurality of user devices as an available user device within the region and receiving the current version of a machine-learned model associated with the region, training of the current version of the machine-learned model associated with the region using data local to the user device;
• and
• providing, to the server, updated machine-learned model data generated through the training of the current version of the machine-learned model.