Toyota Motor Engineering & Manufacturing North America, Inc.

Enes Krijestorac

Onur Altintas

Takamasa Higuchi

Seyhan Ucar

Chang-Heng Wang

Systems and methods for vehicular-network-assisted federated machine learning are disclosed herein. One embodiment transmits first metadata from a connected vehicle to at least one other connected vehicle; receives, at the connected vehicle, second metadata from the at least one other connected vehicle; receives, at the connected vehicle based on analysis of the first and second metadata, a notification that the connected vehicle has been elected to participate in the current training phase of a federated machine learning process; receives, at the connected vehicle, instructions to prepare the connected vehicle for the next training phase; trains 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 submits the locally trained machine learning model for aggregation with at least one other locally trained machine learning model to produce an aggregated locally trained machine learning model.

Systems and methods for vehicular-network-assisted federated machine learning

The disclosed subject matter relates to managing a vehicular micro cloud. A method may include determining join/leave protocols for a vehicular micro cloud and transmitting the join/leave protocols to a local vehicle in a vicinity of the vehicular micro cloud prior to the local vehicle joining the vehicular micro cloud. The join/leave protocols can define at least: 1) a procedure for the local vehicle to join the vehicular micro cloud and contribute computing resources to a collaborative micro cloud task, and 2) a protocol for handing an incomplete task when the local vehicle leaves the vehicular micro cloud.

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Los Angeles, CA, United States of America

Enes Krijestorac