Model training method and apparatus based on data sharing

A non-transitory, computer-readable medium storing one or more instructions executable by a computer system comprising one or more processors to perform operations for data sharing between a data miner and data providers, the operations comprising:

• downloading a first set of public parameters from the data miner to generate training sample data, wherein the first set of public parameters comprises parameters maintained by the data miner that are obtained after the data providers jointly participate in an update and the first set of public parameters is associated with a first feature set of training sample data, wherein the training sample data comprises a plurality of features, and the first feature set is a first subset of the plurality of features allocated by the data miner to a first data provider to be downloaded in parallel with a second subset of the plurality of features allocated by the data miner to a second data provider to satisfy a convergence condition, wherein the first subset of the plurality of features is disjoint from the second subset of the plurality of features;
• replacing a set of private parameters of the first data provider with the first set of public parameters to generate a set of replaced values of the set of private parameters, wherein the set of private parameters comprises data provider parameters maintained solely by the first data provider and wherein the set of private parameters is associated with the first feature set of training sample data;
• updating the set of private parameters to provide a set of update results comprising parameter change values to the set of private parameters and excluding all private parameters of the set of private parameters, the set of private parameters being updated based on a model parameter update algorithm associated with the first data provider, wherein the parameter change values comprise differences between the replaced values of the set of private parameters and the set of update results;
• sorting the set of update results to generate a first sorted set of update results comprising a predetermined number of parameters of the set of update results with the parameter change values that are greater than a predetermined value provided by the data miner;
• generating a first truncated set of update results by truncating the first sorted set of update results based on a predetermined value range;
• and
• uploading the first sorted truncated set of update results to the data miner to be processed with a second wed truncated set of update results uploaded by the second data provider to verify the convergence condition.

A computer-implemented system, comprising:

• one or more computers comprising one or more processors;
• 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 operations for data sharing between a data miner and data providers, the operations comprising:
• downloading a first set of public parameters from the data miner to generate training sample data, wherein the first set of public parameters comprises parameters maintained by the data miner that are obtained after the data providers jointly participate in an update and the first set of public parameters is associated with a first feature set of training sample data, wherein the training sample data comprises a plurality of features, and the first feature set is a first subset of the plurality of features allocated by the data miner to a first data provider to be downloaded in parallel with a second subset of the plurality of features allocated by the data miner to a second data provider to satisfy a convergence condition, wherein the first subset of the plurality of features is disjoint from the second subset of the plurality of features;
• replacing a set of private parameters of the first data provider with the first set of public parameters to generate a set of replaced values of the set of private parameters, wherein the set of private parameters comprises data provider parameters maintained solely by the first data provider and wherein the set of private parameters is associated with the first feature set of training sample data;
• updating the set of private parameters to provide a set of update results comprising parameter change values to the set of private parameters and excluding all private parameters of the set of private parameters, the set of private parameters being updated based on a model parameter update algorithm associated with the first data provider, wherein the parameter change values comprise differences between the replaced values of the set of private parameters and the set of update results;
• sorting the set of update results to generate a first sorted set of update results comprising a predetermined number of parameters of the set of update results with the parameter change values that are greater than a predetermined value provided by the data miner;
• generating a first truncated set of update results by truncating the first sorted set of update results based on a predetermined value range;
• and
• uploading the first truncated set of update results to the data miner to be processed with a second truncated set of update results uploaded by the second data provider to verify the convergence condition.