Deep learning method for distributed multi-objective optimization, computer program, and apparatus therefor

A method, performed by a training device, for training one or more neural network modules by using an objective neural network and a dual neural network, the method comprising:

• acquiring a training data set which includes one or more local observations and one or more priority weights;
• training the dual neural network to output a dual variable for primal-dual optimization based on the priority weights and an output of the objective neural network;
• training the objective neural network to output a global objective of the neural network modules based on the priority weights and an output of the dual neural network;
• and
• training neural network modules to output a message for cooperation with each other and a local solution based on the training data set and the output of the dual neural network.

A computing device for computing a local solution of a multi-objective optimization problem, the device comprising:

• a processor;
• and
• a non-transitory memory storing at least one instruction executed by the processor,
• wherein the processor is configured to:
• generate a first message for cooperation with at least one counterpart computing device based on a local observation and a priority weight;
• transmit the first message to the counterpart computing device;
• receive a second message from the counterpart computing device;
• and
• calculate a local solution for the computing device based on the local observation, the priority weight, and the second message.

A training device for training one or more neural network modules by using an objective neural network and a dual neural network, the training device comprising:

• a processor;
• and
• a non-transitory memory storing at least one instruction executed by the processor,
• wherein the processor is configured to:
• acquire a training data set which includes one or more local observations and one or more priority weights;
• train the dual neural network to output a dual variable for primal-dual optimization based on the priority weights and an output of the objective neural network;
• train the objective neural network to output a global objective of the neural network modules based on the priority weights and an output of the dual neural network;
• and
• train neural network modules to output a message for cooperation with each other and a local solution based on the training data set and the output of the dual neural network.