Abstract: A federated learning method includes: each second device in a plurality of second devices first obtains data distribution information and sends the data distribution information to a first device. The first device receives the data distribution information from the plurality of second devices participating in federated learning. The first device selects a matched federated learning policy based on the data distribution information. The first device sends a parameter reporting policy corresponding to the federated learning to at least one second device in the plurality of second devices. A second device that receives the parameter reporting policy is configured to obtain second gain information based on the parameter reporting policy and a current training sample, and the second gain information is for obtaining a second model of the second device.

Abstract: A sample data annotation system includes an edge node and a central node. The edge node obtains a key feature of sample data, determines, based on the key feature, whether the sample data is unknown sample data, when the sample data is unknown sample data, performs annotation processing on the sample data to obtain a first annotation result, and sends the first annotation result to the central node. The central node receives the first annotation result, and determines whether the first annotation result indicates successful annotation; and when the first annotation result indicates that the unknown sample data is successfully annotated, performs consistency processing on the first annotation result to obtain a second annotation result, or when the annotation result indicates that the unknown sample data fails to be annotated, performs annotation processing on the unknown sample data to obtain a third annotation result.

Abstract: A method, an apparatus, and a computing device for constructing a prediction model, and a storage medium are disclosed, and relate to the field of artificial intelligence technologies. The method includes: obtaining, based on a target dataset of a target prediction scenario and/or scenario information of the target prediction scenario, model search space corresponding to the target prediction scenario; performing model training based on the target dataset and models and hyperparameters that are included in the model search space, to obtain trained prediction models; and obtaining, based on evaluation results of the trained prediction models, a prediction model corresponding to the target prediction scenario. Efficiency of constructing the prediction model can be improved.

Abstract: A modeling method and an apparatus are disclosed. The method includes: obtaining a first data set of a first indicator, and determining, based on the first data set, a second indicator similar to the first indicator; and determining a first model based on one or more second models associated with the second indicator. The first model is used to detect a status of the first indicator, and the status of the first indicator includes an abnormal state or a normal state. The second models are used to detect a status of the second indicator, and the status of the second indicator includes an abnormal state or a normal state.

Abstract: A model update system, which may be applied to the network control field, includes a site analysis device and a first analysis device. The site analysis device is configured to: receive a first model sent by the first analysis device; train the first model by using a first training sample to obtain a second model, where the first training sample includes first feature data of a network device in a site network corresponding to the site analysis device; obtain differential data between the first model and the second model; and send the differential data to the first analysis device. The first analysis device is configured to: send the first model to the site analysis device; receive the differential data sent by the site analysis device; and update the first model based on the differential data to obtain a third model.

Abstract: A label determining method includes: obtaining a target feature vector of a first time series, where a time series is a set of a group of data arranged in a time sequence; obtaining a similarity between the target feature vector and a reference feature vector in a reference feature vector set, where the reference feature vector is a feature vector of a second time series with a determined label; and when a similarity between the target feature vector and a first reference feature vector is greater than a similarity threshold, determining that a label corresponding to the first reference feature vector is a label of the first time series, where the first reference feature vector is a reference feature vector in the reference feature vector set.