Using corrections, of automated assistant functions, for training of on-device machine learning models

A method performed by one or more processors of a client device, the method comprising:

• receiving, via one or more sensor components of the client device, sensor data that captures one or more environmental attributes of an environment of the client device;
• processing the sensor data using a machine learning model, stored locally at the client device, to generate a predicted output that dictates whether one or more currently dormant automated assistant functions are activated;
• making a decision, based on the predicted output failing to satisfy a threshold, as to whether to trigger the one or more currently dormant automated assistant functions;
• subsequent to making the decision:
• receiving, via the client device, further user interface input to initiate one or more of the one or more currently dormant automated assistant functions and that contradicts the decision to refrain from initiating one or more of the one or more currently dormant automated assistant functions;
• and
• determining, based on a duration of time between receiving the sensor data that was processed in making the decision and receiving the further user interface input that contradicts the decision, that the decision to refrain from initiating one or more of the one or more currently dormant automated assistant functions was incorrect;
• and
• in response to determining that the determination was incorrect:
• generating a gradient based on comparing the predicted output to ground truth output that satisfies the threshold, and
• transmitting, over a network and to a remote system, the generated gradient without transmitting any of:
• audio data and the further user interface input,
• wherein the remote system utilizes the generated gradient, and additional gradients from additional client devices, to update global weights of a global machine learning model that is a global counterpart of the machine learning model that is stored locally at the client device.

A client device comprising:

• one or more hardware processors;
• and
• memory storing instructions that, when executed by the one or more hardware processors, cause the one or more hardware processors to:
• receive, via one or more microphones of the client device, audio data that captures a spoken utterance of a user;
• process the audio data using a machine learning model, stored locally at the client device, to generate a predicted output;
• make a decision, based on the predicted output failing to satisfy a threshold, to refrain from initiating one or more currently dormant automated assistant functions;
• subsequent to making the decision to refrain from initiating the one or more currently dormant automated assistant functions:
• receive, via the client device, further user interface input to initiate one or more of the currently dormant automated assistant functions and that contradicts the decision to refrain from initiating one or more of the currently dormant automated assistant functions;
• and
• determine, based on a duration of time between receiving the audio data that was processed in making the decision and receiving the further user interface input that contradicts the decision, that the decision to refrain from initiating one or more of the one or more currently dormant automated assistant functions was incorrect;
• and
• in response to determining that the decision was incorrect:
• generate a gradient based on comparing the predicted output to ground truth output that satisfies the threshold, and
• transmit, over a network and to a remote system, the generated gradient without transmitting any of:
• the audio data and the further user interface input,
• wherein the remote system utilizes the generated gradient, and additional gradients from additional client devices, to update global weights of a global machine learning model that is a global counterpart of the machine learning model that is stored locally at the client device.