# US 12,762,428 B2 · Federated learning for controls and monitoring of functions in vehicular settings > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,762,428 B2 - Document ID: US12762428B2 - Status: Granted - Kind: B2 - Application number: 18/825,975 - Filed: 2024-09-05 - Published: 2025-03-27 - Granted: 2026-10-13 - Earliest claimed: 2023-09-27 - Assignees: Cummins Inc. - Applicants: Cummins Inc. - Inventors: Vishnu Pandi Chellapandi; Hoseinali Borhan; Joshua Edward Supplee - Web page: https://patentgrep.com/patent/US12762428B2 ## CPC - G06N 3/098: Distributed learning, e.g. federated learning - G05B 13/0265: {the criterion being a learning criterion} - G06N 20/00: Machine learning - B60W 10/06: including control of combustion engines - B60W 10/08: including control of electric propulsion units, e.g. motors or generators - B60W 10/30: including control of auxiliary equipment, e.g. air-conditioning compressors or oil pumps - B60W 2050/0088: {Adaptive recalibration} - B60W 50/0205: {Diagnosing or detecting failures; Failure detection models} - B60W 2556/45: External transmission of data to or from the vehicle - G06N 3/045: Combinations of networks - G07C 5/0808: {Diagnosing performance data (testing of vehicles G01M17/00; testing of electrical installation on vehicles G01R31/005)} - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G05B: CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS (systems for controlling or regulating non-electric variables G05D; systems for regulating electric or magnetic variables G05F; control devices or systems insofar as characterised by mechanical features only G05G) - B60W: CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT - G07C: TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE ## Abstract Presented herein are systems and methods for performing federated learning across vehicles. A computing device having one or more processors coupled with memory can maintain, on the memory, a first machine learning (ML) comprising a first plurality of parameters for determining values identifying a characteristic of a vehicle function on at least one of a plurality of vehicles. The computing device can receive a second plurality of parameters generated by a second ML used by each respective vehicle. The computing device can update the first plurality of parameters of the first ML in accordance with the second plurality of parameters received from each respective vehicle of the plurality of vehicles. The computing device can transmit, to a vehicle, the updated first plurality of parameters to update the second plurality of parameters of the second ML on the vehicle. ## Claims ### Claim 1 A system for performing federated learning across vehicles, comprising: a first computing device having one or more processors coupled with memory, the first computing device configured to: maintain, in the memory, a first machine learning (ML) model comprising a first plurality of parameters for determining values identifying a characteristic of a vehicle function on at least one of a plurality of vehicles; receive, 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 device on each respective vehicle using (i) data associated with the vehicle function acquired via at least one sensor and (ii) a value identifying the characteristic of the vehicle function on the respective vehicle; update 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 transmit, to at least one vehicle of the plurality of vehicles, an identifier corresponding the vehicle function and the updated first plurality of parameters to update the second plurality of parameters of the second ML model on the at least one vehicle. ### Claim 2 (depends on 1) The system of claim 1, wherein the first computing device is further configured to retrieve, responsive to establishing a connection with at least one vehicle of the plurality of vehicles, the second plurality of parameters of the second ML model on the at least one vehicle, wherein the second plurality of parameters are generated on the at least one vehicle while no connection was established with the first computing device. ### Claim 3 (depends on 1) The system of claim 1, wherein the first computing device is further configured to initialize the first ML model using a third plurality of parameters generated by a third ML model maintained on at least one server. ### Claim 4 (depends on 1) The system of claim 1, wherein the first computing device is further configured to identify, from a plurality of ML models, the first ML model based on the vehicle function. ### Claim 5 (depends on 1) The system of claim 1, wherein the first computing device is further configured to: generate an output using the first ML model that identifies a vehicle function action; and cause, in accordance with the output, one or more components associated with the vehicle function action to execute the vehicle function action. ### Claim 6 (depends on 1) The system of claim 1, wherein the vehicle function comprises at least one of: a battery management function to control output from a battery, a dosing control function of an aftertreatment system to control an injection of reductant in the aftertreatment system, an engine control function, a hybrid control function to control a switching between electric motor and fuel-based engine, an advanced drive assistance system function to control operation of vehicle movement, a vehicle diagnostic function to detect faults in the vehicle, or a vehicle prognostic function to predict faults in the vehicle. ### Claim 7 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. ### Claim 8 (depends on 7) The vehicle system of claim 7, wherein the at least one processing circuit is further configured to: receive, from the at least one computing device, a second plurality of parameters of the second ML model, the second plurality of parameters generated by the at least one computing device using the first plurality of parameters and a third plurality of parameters received from each respective vehicle system of a plurality of vehicle systems; identify, from a plurality of ML models, the first ML model based on the vehicle system function; and update the first plurality of parameters of the first ML model using the third plurality of parameters. ### Claim 9 (depends on 7) The vehicle system of claim 7, wherein the at least one processing circuit is further configured to transmit compressed data regarding the first plurality of parameters to the at least one computing device in response to establishment of a connection with the at least one computing device. ### Claim 10 (depends on 7) The vehicle system of claim 7, wherein the at least one processing circuit is further configured to initialize the first ML model using the second plurality of parameters received from the second ML model of the at least one computing device remote from the at least one processing circuit. ### Claim 11 (depends on 7) The vehicle system of claim 7, wherein the at least one computing device is at least one server, and wherein the at least one processing circuit is further configured to transmit the first plurality of parameters to the at least one server to update a third ML model on the at least one server using the first plurality of parameters and a respective second plurality of parameters from each respective vehicle system of a plurality of vehicle systems. ### Claim 12 (depends on 7) The vehicle system of claim 7, wherein the at least one computing device is an on-board vehicle computing device, and wherein the at least one processing circuit is further configured to transmit the first plurality of parameters to the on-board vehicle computing device to update the second ML model using the first plurality of parameters. ### Claim 13 (depends on 7) The vehicle system of claim 7 wherein the at least one processing circuit is further configured to: generate an output using the first ML model that identifies a vehicle function action; and cause, in accordance with the output, one or more components associated with the vehicle function action to execute the vehicle function action. ### Claim 14 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. ### Claim 15 (depends on 14) The method of claim 14, wherein receiving the second plurality of parameters further comprises retrieving, by the first computing system and responsive to establishing a connection with at least one vehicle of the plurality of vehicles, the second plurality of parameters of the second ML model on the at least one vehicle, wherein the second plurality of parameters are generated on the at least one vehicle while no connection was established with the first computing system. ### Claim 16 (depends on 14) The method of claim 14, further comprising initializing, by the first computing system, the first ML model using a third plurality of parameters generated by a third ML model maintained on at least one server. ### Claim 17 (depends on 14) The method of claim 14, further comprising identifying, by the first computing system, the first ML model from a plurality of ML models based on the vehicle function. ### Claim 18 (depends on 14) The method of claim 14, further comprising: generating, by the first computing system, an output using the first ML model that identifies a vehicle function action; and causing, by the first computing system, in accordance with the output, one or more components associated with the vehicle function action to execute the vehicle function action.