Linear compression of gradients in federated machine learning model training

A method for wireless communications at a network entity, comprising:

• transmitting, to a plurality of user equipments (UEs), an indication of a linear compression matrix associated with a machine learning model;
• transmitting, to the plurality of UEs, an indication of a wireless communication resource to be used by the plurality of UEs for concurrent transmission of compressed gradient data that is compressed using the linear compression matrix;
• receiving, via the wireless communication resource, a first signal comprising an over-the-air (OTA) summation of the compressed gradient data;
• and
• transmitting, to the plurality of UEs, a second signal comprising the OTA summation of the compressed gradient data or indicating an update to the machine learning model based at least in part on the compressed gradient data.

A user equipment (UE), comprising:

• one or more memories storing processor-executable code;
• and
• one or more processors coupled with the one or more memories and individually or collectively operable to execute the code to cause the UE to:
• receive an indication of a linear compression matrix associated with a machine learning model and associated with a plurality of UEs comprising the UE;
• receive an indication of a wireless communication resource to be used by the plurality of UEs for concurrent transmission of compressed gradient data that is compressed using the linear compression matrix;
• transmit a first signal comprising first compressed gradient data that is compressed using the linear compression matrix;
• and
• receive, based at least in part on the first signal, a second signal comprising an over-the-air (OTA) summation of compressed gradient data or indicating an update to the machine learning model based at least in part on the OTA summation of the compressed gradient data.