US 12,337,867 B2
Systems and methods for communication-aware federated learning
Yitao Chen, Mountain View, CA (US); Dawei Chen, Mountain View, CA (US); Haoxin Wang, Mountain View, CA (US); and Kyungtae Han, Mountain View, CA (US)
Assigned to Toyota Motor Engineering & Manufacturing North America, Inc., Plano, TX (US); and Toyota Jidosha Kabushiki Kaisha, Toyota (JP)
Filed by Toyota Motor Engineering & Manufacturing North America, Inc., Plano, TX (US)
Filed on Nov. 28, 2022, as Appl. No. 17/994,850.
Prior Publication US 2024/0174254 A1, May 30, 2024
Int. Cl. B60W 60/00 (2020.01); G06F 18/214 (2023.01)
CPC B60W 60/001 (2020.02) [G06F 18/214 (2023.01); B60W 2420/403 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A vehicle comprising:
a controller programmed to:
train a machine learning model using first local data;
obtain a network bandwidth for a channel between the vehicle and a server;
determine a level of compression based on the network bandwidth for the channel;
compress the trained machine leaning model based on the determined level of compression;
transmit the compressed trained machine learning model to the server;
receive an aggregated machine learning model from the server; and
control the vehicle to drive autonomously based on the aggregated machine learning model.