Computer-Implemented Data Structure, Method and System for Operating a Technical Device with a Model Based on Federated Learning

A computer-implemented method for operating a technical device connected to a client of a client-server system, the client-server system comprising a server and connected clients which interact at least temporarily with the server, which is captured as interactions, with a model based on federated learning, the method comprising:

• a) capturing, by the client, a reliability factor which is based on an availability of the technical device during operation of the client-server system and which is ascertained from the captured interactions;
• b) capturing, by the client, a response time factor which is based on a time difference between requesting information and receiving a response to the request for the technical device during the operation of the client-server system and which is ascertained from the captured interactions;
• c) capturing, by the client, a quality-of-information factor which is based at least on an accuracy, completeness, consistency, up-to-dateness, validity or uniqueness of information for the technical device during the operation of the system and which is ascertained from the captured interactions;
• d) determining, by the client, a respective trust factor which is formed as a model comprising a Bayesian network, for the clients, which is formed from the reliability factor, the response time factor and the quality-of-information factor aided by factors for respective probabilities;
• e) training local models in respective clients;
• f) transmitting the trained local models from the respective client to the server;
• g) aggregating, by the server, the transmitted local models of the respective clients into a global model by applying the respective trust factor to the respective local model;
• and
• h) transmitting the global model from the server to the client and actuating the technical device aided by the global model.

A system for operating a technical device with a model based on federated learning, comprising:

• a server;
• and
• clients connected to the server, the clients each being connected to technical devices;
• wherein the system is configured to:
• a) capture, by the client, a reliability factor which is based on an availability of the technical device during operation of the client-server system and which is ascertained from the captured interactions;
• b) capture, by the client, a response time factor which is based on a time difference between requesting information and receiving a response to the request for the technical device during the operation of the client-server system and which is ascertained from the captured interactions;
• c) capture, by the client, a quality-of-information factor which is based at least on an accuracy, completeness, consistency, up-to-dateness, validity or uniqueness of information for the technical device during the operation of the system and which is ascertained from the captured interactions;
• d) determine, by the client, a respective trust factor which is formed as a model comprising a Bayesian network, for the clients, which is formed from the reliability factor, the response time factor and the quality-of-information factor aided by factors for respective probabilities;
• e) train local models in respective clients;
• f) transmit the trained local models from the respective client to the server;
• g) aggregate, by the server, the transmitted local models of the respective clients into a global model by applying the respective trust factor to the respective local model;
• and
• h) transmit the global model from the server to the client and actuate the technical device aided by the global model.