US 12,258,045 B2
Systems and methods for vehicular-network-assisted federated machine learning
Seyhan Ucar, Mountain View, CA (US); Takamasa Higuchi, Mountain View, CA (US); Chang-Heng Wang, Mountain View, CA (US); Enes Krijestorac, Los Angeles, CA (US); and Onur Altintas, Mountain View, CA (US)
Assigned to Toyota Motor Engineering & Manufacturing North America, Inc., Plano, TX (US)
Filed by Toyota Motor Engineering & Manufacturing North America, Inc., Plano, TX (US)
Filed on Feb. 5, 2021, as Appl. No. 17/168,406.
Prior Publication US 2022/0250656 A1, Aug. 11, 2022
Int. Cl. B60W 60/00 (2020.01); G01C 21/34 (2006.01); G06F 18/214 (2023.01); G06N 20/00 (2019.01); G06V 20/56 (2022.01); H04W 4/46 (2018.01)
CPC B60W 60/00184 (2020.02) [G01C 21/343 (2013.01); G06F 18/214 (2023.01); G06F 18/2148 (2023.01); G06N 20/00 (2019.01); G06V 20/56 (2022.01); H04W 4/46 (2018.02)] 16 Claims
OG exemplary drawing
 
1. A system for vehicular-network-assisted federated machine learning, the system comprising:
a processor; and
a memory storing computer-readable instructions that, when executed by the processor, cause the processor to:
exchange metadata between a connected vehicle and at least one other connected vehicle in a vehicular micro cloud, wherein the metadata includes information regarding sensor capabilities of the connected vehicle and the at least one other connected vehicle;
receive, at the connected vehicle based on an analysis of the metadata, a notification that the connected vehicle has been elected to participate in a current training phase of a federated machine learning process;
receive, at the connected vehicle, instructions that position the connected vehicle to provide information about an object of interest to the federated machine learning process;
train a machine learning model to perform a task at the connected vehicle during the current training phase to produce a locally trained machine learning model; and
submit the locally trained machine learning model for aggregation with at least one other locally trained machine learning model produced by at least one other elected vehicle in the vehicular micro cloud to produce an aggregated locally trained machine learning model that is used as a starting point for a next training phase of the federated machine learning process.