Abstract: A model construction method and an apparatus, and a medium and an electronic device are disclosed. The method is applied to a first participant platform, and includes: associating first configuration information pre-created by a first participant with second configuration information pre-created by a second participant; verifying the first configuration information; sending, to a second participant platform corresponding to the second participant, a second creation request for requesting the creation of the federated learning model, to cause the second participant platform to verify the second configuration information creating a first model task on the basis of a first parameter corresponding to the first configuration information; and performing co-training on the basis of the first model task and a second model task, to obtain the federated learning model.

Abstract: The present disclosure relates to a data processing method, non-transitory medium and electronic device. The method includes: acquiring first user data and second user data, and initializing a first time window corresponding to the first user data and first time information corresponding to the first time window, as well as a second time window corresponding to the second user data and second time information corresponding to the second time window; determining first data and the first time information corresponding to the first time window based on the first user data; determining second data and the second time information corresponding to the second time window based on the second user data; based on the first data and the second data, determining alignment data corresponding to a same user from the first user data and the second user data.

Abstract: Provided are a method for processing model parameters, and an apparatus. The method comprises: a model parameter set to be sharded is obtained, wherein the model parameter set comprises a multi-dimensional array corresponding to a feature embedding; attribute information for a storage system used for storing the model parameter set to be sharded is obtained, wherein the storage system used for storing the model parameter set to be sharded differs from a system on which a model corresponding to the model parameter set to be sharded is located when operating; the model parameter set to be sharded is stored in the storage system according to the attribute information.

Abstract: A network connection method and device for a training participant of a joint training model are provided, the training participant operates in a master-worker mode. The method includes: acquiring communication state information of a worker, the communication state information indicating a communication connection phase that the worker is in; acquiring communication state information of a target worker as target communication state information, where the target worker includes a peer node corresponding to the worker, and the peer node belongs to a different training participant of the joint training model; and resetting, in response to determining that the target communication state information does not match the communication state information of the worker, a communication connection phase that the worker is in.

Abstract: Provided are a method for processing model parameters, and an apparatus. The method comprises: a model parameter set to be sharded is obtained, wherein the model parameter set comprises a multi-dimensional array corresponding to a feature embedding; attribute information for a storage system used for storing the model parameter set to be sharded is obtained, wherein the storage system used for storing the model parameter set to be sharded differs from a system on which a model corresponding to the model parameter set to be sharded is located when operating; the model parameter set to be sharded is stored in the storage system according to the attribute information.

Abstract: Provided are a method and apparatus for evaluating a joint training model. A specific implementation of the method for evaluating a joint training model comprises: receiving a model evaluation data request sent by a target device, wherein the target device comprises a participant of a joint training model; acquiring a sample set matching the model evaluation data request, wherein the matching sample set is labeled data associated with the joint training model; and generating model evaluation data of the joint training model according to the matching sample set. By means of the implementation, an effect index of a joint training model can be shared on the premise of not exposing original sample data. Accordingly, a timely and effective data reference basis is provided for the optimization and improvement of the joint training model.

Abstract: A network connection method and device for a training participant of a joint training model are provided, the training participant operates in a master-worker mode. The method includes: acquiring communication state information of a worker, the communication state information indicating a communication connection phase that the worker is in; acquiring communication state information of a target worker as target communication state information, where the target worker includes a peer node corresponding to the worker, and the peer node belongs to a different training participant of the joint training model; and resetting, in response to determining that the target communication state information does not match the communication state information of the worker, a communication connection phase that the worker is in.

Abstract: Provided are a method and an apparatus for generating prediction information, an electronic device, and a computer readable medium. The method includes: generating, based on first user characteristic information of a target user, anonymous user information of the target user (201); sending the anonymous user information to a second processing end to enable the second processing end to generate prediction information based on the anonymous user information and second user characteristic information (202) of the target user. Data interaction and sharing are realized while ensuring data privacy, thereby improving accuracy of the prediction information.