Federated learning for controls and monitoring of functions in vehicular settings

A vehicle system, comprising:

• a component configured to perform a vehicle system function;
• and
• at least one processing circuit including one or more processors coupled with memory, the at least one processing circuit configured to:
• maintain a first machine learning (ML) model comprising a first plurality of parameters for determining values associated with the vehicle system function;
• identify data associated with the vehicle system function and a first value identifying a characteristic of the vehicle system function;
• generate a second value identifying the characteristic of the vehicle system function using the first ML model;
• update the first plurality of parameters of the first ML model based on a comparison between the first value and the second value;
• and
• transmit an identifier corresponding the vehicle system function and the updated first plurality of parameters from the ML model to at least one computing device remote from the at least one processing circuit to update a second plurality of parameters of a second ML model maintained by the at least one computing device.

A method of performing federated learning across vehicles, the method comprising:

• maintaining, by a first computing system, a first machine learning (ML) model comprising a first plurality of parameters for determining values associated with a vehicle function of at least one of a plurality of vehicles;
• receiving, by the first computing system and from each respective vehicle of the plurality of vehicles, a second plurality of parameters generated by a second ML model used by a second computing system on each respective vehicle;
• updating, by the first computing system, the first plurality of parameters of the first ML model in accordance with the second plurality of parameters received from each respective vehicle of the plurality of vehicles;
• and
• transmitting, by the first computing system and to at least one vehicle of the plurality of vehicles, an identifier corresponding the vehicle function and the updated first plurality of parameters to cause a second computing system on the at least one vehicle to update the second plurality of parameters of the second ML model on the at least one vehicle.