US 12,470,920 B2
Systems and methods for data security in collaborative machine learning of autonomous vehicles
Sharath Yadav Doddamane Hemantharaja, Karnataka (IN)
Assigned to HARMAN INTERNATIONAL INDUSTRIES, INCORPORATED, Stamford, CT (US)
Appl. No. 18/005,651
Filed by Harman International Industries, Incorporated, Stamford, CT (US)
PCT Filed Sep. 15, 2020, PCT No. PCT/US2020/050921
§ 371(c)(1), (2) Date Jan. 16, 2023,
PCT Pub. No. WO2022/019932, PCT Pub. Date Jan. 27, 2022.
Claims priority of application No. 202011031177 (IN), filed on Jul. 21, 2020.
Prior Publication US 2023/0292117 A1, Sep. 14, 2023
Int. Cl. H04L 9/00 (2022.01); H04L 9/40 (2022.01); H04W 12/0433 (2021.01); H04W 4/40 (2018.01)
CPC H04W 12/0433 (2021.01) [H04L 63/062 (2013.01)] 15 Claims
OG exemplary drawing
 
1. A method, comprising:
generating, in a vehicle, a digital signature based on a first key and a second key, both of the first key and the second key received from a group manager, and transmitting a message signed with the digital signature to a collaborator, the message including a subset of coefficients of a local machine learning model of the vehicle, wherein the subset corresponds to a vehicle system functionality; and
receiving an updated machine learning model from the collaborator and adjusting the local machine learning model based on the updated machine learning model, wherein:
adjusting the local machine learning model based on the updated machine learning model includes adjusting at least one coefficient of the local machine learning model;
the updated machine learning model is updated by the collaborator based on data from the vehicle and a plurality of collaborating vehicles of a collaborative machine learning network, wherein each of the plurality of collaborating vehicles uploads a plurality of local machine learning models corresponding to a plurality of vehicle systems to a cloud server, wherein the cloud server analyzes the uploaded machine learning models from the plurality of vehicles, and constructs updated machine learning models, wherein, based on coefficients from each of the local machine learning models, the cloud server determines an updated global machine learning model for each vehicle system, which incorporates learning from each local machine learning model, so that data from each of the plurality of vehicles is incorporated into the global machine learning models, wherein the updated global machine learning model is shared with each of the plurality of vehicles so that the updated global machine learning model replaces local machine learning models, and wherein operation continues, where each of the vehicles continues to train and update their respective local machine learning models, providing continuous, collaborative updates to the machine learning models for each vehicle; and
the first key is a group public key, the group public key distributed to each of the plurality of collaborating vehicles and the collaborator by the group manager, and wherein an unauthorized vehicle that participates in the network has limited ability to provide inaccurate data to the cloud server to data regarding other vehicles in the network by the method removing the unauthorized vehicle by a revocation authority in a location remote from each of the plurality of vehicles.