Method and system for predicting fuel consumption for a flight of an aircraft

A method for predicting a fuel consumption for a flight of an aircraft from a departure airport to an arrival airport, the method comprising:

• accessing, by one or more processors in communication with a non-transitory computer readable medium having executable instructions therein, a plurality of data points comprising historical flights from the departure airport to the arrival airport, wherein, upon execution of the executable instructions, the one or more processors are configured to perform steps comprising:
• building a central machine learning model for predicting the fuel consumption for the flight of the aircraft using the data points, the central machine learning model built using a machine learning algorithm and one or more of the plurality of data points;
• generating, by the central machine learning model, a first prediction of the fuel consumption for the flight of the aircraft;
• distributing a version of the central machine learning model along with the first prediction of the fuel consumption to one or more edge computing devices, each of the one or more edge computing devices being associated with an organization that operates the aircraft, and being in communication with a data store comprising organization-specific data usable by the one or more edge computing devices associated with the organization to train the version of the central machine learning model received thereby with the organization-specific data;
• receiving, at the central machine learning model from the one or more edge computing devices, a neural network gain between the first prediction and an organization-specific prediction of the fuel consumption for the flight of the aircraft generated by the version of the central machine learning model trained with the organization-specific data, the neural network gain being based on a weighted difference between the first prediction and the organization-specific prediction and an actual fuel consumption for the flight from the departure airport to the arrival airport;
• updating the central machine learning model based on the neural network gain from the one or more edge computing devices to generate a revised prediction of the fuel consumption for the flight of the aircraft;
• and
• receiving, by the central machine learning model, edge neural network gains from two or more edge computing devices, wherein updating the central machine learning model based on the neural network gain comprises:
• determining an accuracy of the organization-specific prediction generated by determining a difference between the organization-specific prediction and the actual fuel consumption for the flight;
• and
• calculating and assigning different weights to the edge neural network gains received from the two or more edge computing devices based on the difference,
• wherein the neural network gain is calculated using a weighted average of the edge neural network gains received from the two or more edge computing devices based on the different weights assigned to the two or more edge computing devices, and
• wherein updating the central machine learning model based on the neural network gain comprises training the central machine learning model to generate the revised prediction using the neural network gain calculated using the weighted average.