Federated learning for controls and monitoring of functions in vehicular settings

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.