A method for collaborative machine learning of analytical models
AssigneeSiemens Aktiengesellschaft
InventorsJan-Gregor Fischer, Denis Krompaß, Josep Soler Garrido
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Abstract
Provided is a method for machine learning of analytical models, AMs, including core model components, CMCs, shared between tasks, t, of different customers and including specialized model components, SMCs, specific to customer tasks, t, of individual customers, wherein the machine learning of the analytical models, AMs, is performed collaboratively based on local data, LD, provided by machines of the customer premises of different customers without the local data, LD, leaving the respective customer premises.
Claims17
A method for machine learning of analytical models, AMs, comprising core model components, CMCs, shared between tasks, t, of different customers and comprising specialized model components, SMCs, specific to customer tasks, t, of individual customers,
- wherein the machine learning of the analytical models, AMs, is performed collaboratively based on local data, LD, provided by machines of the customer premises of different customers without the local data, LD, leaving the respective customer premises.
An industrial system comprising
- customer premises of different customers, wherein each customer premises comprises one or more machines providing local data, LD, to customer computing devices having deployed analytical models, AMs, comprising core model components, CMCs, shared between tasks, t, of different customers and specialized model components, SMCs, specific to customer tasks, t, of individual customers;
- and
- a third-party backend adapted to combine candidate core model components, cCMCs, formed by updated shared core model components, CMCs, of the analytical models, AMs, trained on local data, LD, to generate global candidate core model components, gcCMCs, and to replace analytical models, AMs, deployed on assigned customer computing devices by candidate analytical models, cAMs, comprising the generated global candidate core model components, gCMCs, if it is verified that the deployed analytical models, AMs, are outperformed by the corresponding candidate analytical models, cAMs.
Dependencies are inferred from the claim text.