Methods, apparatuses, and systems for updating service model based on privacy protection

A non-transitory, computer-readable medium storing one or more instructions executable by a computer system to perform operations comprising:

• determining, by a plurality of service parties, a plurality of model bases, wherein each model basis of the plurality of model bases comprises a plurality of corresponding reference parameters;
• constructing, by each service party of the plurality of service parties, a respective local service model based on a respective combination of the plurality of model bases;
• determining, by each service party, respective gradient data corresponding to each model basis including processing respective local training samples using the respective local service model;
• sending, by each service party, the respective gradient data to a server;
• determining, by the server, that a first model basis of the plurality of model bases satisfies a gradient update condition pre-stored in the server;
• in response to determining that the first model basis satisfies the gradient update condition, combining, by the server, corresponding gradient data of the first model basis received from each service party to obtain global gradient data corresponding to the first model basis;
• sending, by the server, the global gradient data corresponding to the first model basis to each service party;
• and
• training, by each service party, the respective local service model including updating reference parameters in local model basis corresponding to the first model basis using the global gradient data.

A computer-implemented system, comprising:

• one or more computers;
• and
• one or more computer memory devices interoperably coupled with the one or more computers and having tangible, non-transitory, machine-readable media storing one or more instructions that, when executed by the one or more computers, perform one or more operations comprising:
• determining, by a plurality of service parties, a plurality of model bases, wherein each model basis of the plurality of model bases comprises a plurality of corresponding reference parameters;
• constructing, by each service party of the plurality of service parties, a respective local service model based on a respective combination of the plurality of model bases;
• determining, by each service party, respective gradient data corresponding to each model basis including processing respective local training samples using the respective local service model;
• sending, by each service party, the respective gradient data to a server;
• determining, by the server, that a first model basis of the plurality of model bases satisfies a gradient update condition pre-stored in the server;
• in response to determining that the first model basis satisfies the gradient update condition, combining, by the server, corresponding gradient data of the first model basis received from each service party to obtain global gradient data corresponding to the first model basis;
• sending, by the server, the global gradient data corresponding to the first model basis to each service party;
• and
• training, by each service party, the respective local service model including updating reference parameters in local model basis corresponding to the first model basis using the global gradient data.