Federated training for a neural network with reduced communication requirement

A method for generating a training contribution for a neural network on a client node for a federated training of the neural network, the method comprising the following steps:

• receiving a complete set of parameters that characterize a behavior of the neural network from a server node;
• supplying the neural network parameterized with the set of parameters with training examples from a predefined set so that the neural network in each case delivers outputs, wherein the training examples are each labeled with target outputs' evaluating deviations of the outputs from the respective target outputs with a predefined cost function;
• optimizing the parameters of the neural network are optimized with a goal of ensuring that, during further processing of training examples, the evaluation by the cost function is improved;
• selecting a set of particularly relevant parameters based on a predefined criterion;
• for the selected parameters, ascertaining proposed changes as the training contribution based on a result of the optimization;
• and
• transmitting the proposed changes to the server node.

A method for a federated training of a neural network, comprising the following steps:

• initialing, by a server node, a complete set of parameters that characterize a behavior of the neural network;
• distributing the complete set of parameters, by the server node, to a plurality of client nodes, the client nodes ascertaining proposed changes for respectively selected parameters and sending the proposed changes to the server node;
• and
• aggregating the proposed changes by the server node to form a change of the set of parameters.

A non-transitory machine-readable data carrier on which is stored a computer program for generating a training contribution for a neural network on a client node for a federated training of the neural network, the computer program, when executed by one or more computers and/or compute instances, causing the one or more computers and/or compute instances to perform the following steps:

• receiving a complete set of parameters that characterize a behavior of the neural network from a server node;
• supplying the neural network parameterized with the set of parameters with training examples from a predefined set so that the neural network in each case delivers outputs, wherein the training examples are each labeled with target outputs'
• evaluating deviations of the outputs from the respective target outputs with a predefined cost function;
• optimizing the parameters of the neural network are optimized with a goal of ensuring that, during further processing of training examples, the evaluation by the cost function is improved;
• selecting a set of particularly relevant parameters based on a predefined criterion;
• for the selected parameters, ascertaining proposed changes as the training contribution based on a result of the optimization;
• and
• transmitting the proposed changes to the server node.

One or more computers and/or compute instances equipped with a non-transitory machine-readable data carrier on which is stored a computer program for generating a training contribution for a neural network on a client node for a federated training of the neural network, the computer program, when executed by the one or more computers and/or compute instances, causing the one or more computers and/or compute instances to perform the following steps:

• receiving a complete set of parameters that characterize a behavior of the neural network from a server node;
• supplying the neural network parameterized with the set of parameters with training examples from a predefined set so that the neural network in each case delivers outputs, wherein the training examples are each labeled with target outputs'
• evaluating deviations of the outputs from the respective target outputs with a predefined cost function;
• optimizing the parameters of the neural network are optimized with a goal of ensuring that, during further processing of training examples, the evaluation by the cost function is improved;
• selecting a set of particularly relevant parameters based on a predefined criterion;
• for the selected parameters, ascertaining proposed changes as the training contribution based on a result of the optimization;
• and
• transmitting the proposed changes to the server node.