Abstract: Disclosed is a method for managing a local model and a global model on an AI platform, the method performed by one or more processors of a computing device according to an exemplary embodiment of the present disclosure. the method may include: obtaining one or more encoded reference information, generated by encoding task information or data type information related to local training performed by one or more local servers; obtaining at least a part of parameter information of one or more local models locally trained by the one or more local servers; and updating a global model based on the one or more encoded reference information and at least a part of the parameter information of the one or more local models.

Abstract: Disclosed is a method for managing a local model and a global model on an AI platform, the method performed by one or more processors of a computing device according to an exemplary embodiment of the present disclosure. the method may include: obtaining a plurality of sample data; receiving a first user input for a first data set included in the plurality of sample data; receiving a second user input for a second data set excluding the first data set among the plurality of sample data; and obtaining a supplemented second data set by supplementing the second user input based on the first user input.