Abstract: A prediction model training method includes transmitting a model to be trained by a plurality of training devices, the model to be trained including feature extraction layers and prediction layers, classifying the plurality of training devices into at least one group based on extracted user features, receiving, from the plurality of training devices, model parameters including first parameters corresponding to the feature extraction layers and second parameters corresponding to the prediction layers, performing global federated aggregation based on the first parameters, performing intra-group federated aggregation for each of the at least one group, based on the second parameters of one or more of the plurality of training devices in a respective group, and transmitting, to the plurality of training devices, the global federated aggregation result and the intra-group federated aggregation result.

Abstract: The embodiment of the present disclosure provides a method, an apparatus, an electronic device, and a storage medium for predicting user attribute. The method comprises acquiring an input of demographic-related information; determining features corresponding to the input of the demographic-related information and a weight corresponding to each feature, where the features comprise a single feature corresponding to each input of the demographic-related information, and/or a cross feature between the at least two single features; predicting user attribute according to each feature and its corresponding weight. Based on the solution provided by the embodiment of the present disclosure, the accuracy on the prediction of the user attribute can be effectively improved.

Abstract: The embodiment of the present disclosure provides a method, an apparatus, an electronic device, and a storage medium for predicting user attribute. The method comprises acquiring an input of demographic-related information; determining features corresponding to the input of the demographic-related information and a weight corresponding to each feature, where the features comprise a single feature corresponding to each input of the demographic-related information, and/or a cross feature between the at least two single features; predicting user attribute according to each feature and its corresponding weight. Based on the solution provided by the embodiment of the present disclosure, the accuracy on the prediction of the user attribute can be effectively improved.