Techniques for efficient end-to-end machine learning in multi-user systems

A method performed by a base station, the method comprising:

• storing a machine learning (ML) model and a set of ML parameters associated with the ML model;
• generating a first set of forward-pass values based on a first perturbation of the set of ML parameters by a random vector in a positive direction;
• generating a second set of forward-pass values based on a second perturbation of the set of ML parameters by the random vector in a negative direction;
• transmitting a set of training messages to a corresponding set of user equipments (UEs), wherein each training message comprises the first set of forward-pass values and the second set of forward-pass values;
• receiving a set of loss difference values, wherein each loss difference value is associated with a UE of the set of UEs;
• determining a model update decision based on the set of loss difference values;
• and
• transmitting, to the set of UEs, an update message indicating the model update decision.

A user equipment (UE) for wireless communication, comprising:

• at least one memory;
• and
• at least one processor coupled with the at least one memory and configured to cause the UE to:
• store a machine learning (ML) model and a set of ML parameters associated with the ML model;
• receive, from a base station, a training message comprising a first set of forward-pass values and a second set of forward-pass values, wherein the first set of forward-pass values corresponds to a first perturbation of the set of ML parameters in a positive direction and the second set of forward-pass values corresponds to a second perturbation of the set of ML parameters in a negative direction;
• determine a loss difference value based on a random vector and the training message;
• transmit, to the base station, a feedback message comprising the loss difference value;
• and
• receive, from the base station, an update message indicating a model update decision.

A processor for wireless communications, comprising:

• at least one controller coupled with at least one memory and configured to cause the processor to:
• store a machine learning (ML) model and a set of ML parameters associated with the ML model;
• receive, from a base station, a training message comprising a first set of forward-pass values and a second set of forward-pass values, wherein the first set of forward-pass values corresponds to a first perturbation of the set of ML parameters in a positive direction and the second set of forward-pass values corresponds to a second perturbation of the set of ML parameters in a negative direction;
• determine a loss difference value based on a random vector and the training message;
• transmit, to the base station, a feedback message comprising the loss difference value;
• and
• receive, from the base station, an update message indicating a model update decision.