Abstract: An unmanned aerial vehicle cluster system, a method, apparatus, and system of launching, and a readable medium. The unmanned aerial vehicle cluster includes a plurality of unmanned aerial vehicles located in a launch area. The method of launching the unmanned aerial vehicle cluster includes: acquiring (S210) launch positions for the plurality of unmanned aerial vehicles in the launch area; acquiring (S220) an assembly area which corresponds to the launch area and includes a plurality of target positions; determining (S230) a target position for each unmanned aerial vehicle of the plurality of unmanned aerial vehicles according to the launch positions for the plurality of unmanned aerial vehicles and the plurality of target positions; and launching (S240) the plurality of unmanned aerial vehicles according to the launch position for each unmanned aerial vehicle of the plurality of unmanned aerial vehicles and the target position for each unmanned aerial vehicle.

Abstract: Embodiments of this specification provide methods, apparatuses systems, and computer-readable media for data privacy-preserving training of a service prediction model. In an example training process, a member device performs prediction by using the service prediction model and object feature data held by the member device, and determines, by using a prediction result, update parameters used to update model parameters, where the update parameters include sub-parameters for computational layers of the service prediction model; and divides the computational layers into first-type and second-type computational layers using the sub-parameters; and performs privacy processing on sub-parameters of the first-type computational layers, and outputs processed sub-parameters. Processed sub-parameters of member devices can be aggregated into aggregated sub-parameters.

Abstract: This specification provides example computer-implemented methods and apparatuses for jointly updating a service model based on privacy protection. In an example iteration process, a serving party provides, to each data party, global model parameters and a mapping relationship between the data party and N parameter groups obtained by dividing the global model parameters. Each data party updates a local service model by using the global model parameters, and further updates an updated local service model based on local service data, to upload model parameters in a new service model in a parameter group corresponding to the data party to the serving party. Then, the serving party successively fuses received parameter groups to update the global model parameters.

Abstract: Embodiments of this specification provide computer-implemented methods, apparatuses, and systems for training a model using multiple data owners. In an example method, a second data owner determines, according to first data, second feature data intersecting the first data. The following main iteration process is performed until an iteration end condition is met: performing, for each training unit by using a first training sample and a second training sample, cooperative training on a first model, a second model, and a third model that participate in training of the training unit. A master server performs federated aggregation on the trained first model and/or third model in each training unit, to obtain a corresponding first global model and/or third global model. The first model is updated according to the first global model and/or the third model is updated according to the third global model.

Abstract: A computer-implemented method, medium, and system are disclosed. One example computer-implemented method performed by a server includes obtaining training task information from a task party. The training task information includes information about a to-be-pretrained model and information about a to-be-trained target model. A respective task acceptance indication from each of at least one of a plurality of data parties is received to obtain a candidate data party set. The information about the to-be-pretrained model is sent to each data party in the candidate data party set. A respective pre-trained model of each data party is received. A respective performance parameter of the respective pre-trained model of each data party is obtained. One or more target data parties from the candidate data party set is determined. The information about the to-be-trained target model is sent to the one or more target data parties to obtain a target model.

Abstract: A method for training a neural network model includes: at each first member device, obtaining predicted label data according to a first neural network submodel of the first neural network submodel by using private data for model training, and determining model update information of the first neural network submodel according to the predicted label data and real label data; providing, by each first member device, the model update information of the first neural network submodel and local sample distribution information to a second member device; at the second member device, performing neural network model reconstruction, determining an overall sample probability distribution, and allocating a reconstructed neural network model and the overall sample probability distribution to each first member device; and updating the first neural network submodel at each first member device according to a local sample probability distribution, the reconstructed neural network model, and the overall sample probability distrib

Abstract: An unmanned aerial vehicle cluster system, a method, apparatus, and system of launching, and a readable medium. The unmanned aerial vehicle cluster includes a plurality of unmanned aerial vehicles located in a launch area. The method of launching the unmanned aerial vehicle cluster includes: acquiring (S210) launch positions for the plurality of unmanned aerial vehicles in the launch area; acquiring (S220) an assembly area which corresponds to the launch area and includes a plurality of target positions; determining (S230) a target position for each unmanned aerial vehicle of the plurality of unmanned aerial vehicles according to the launch positions for the plurality of unmanned aerial vehicles and the plurality of target positions; and launching (S240) the plurality of unmanned aerial vehicles according to the launch position for each unmanned aerial vehicle of the plurality of unmanned aerial vehicles and the target position for each unmanned aerial vehicle.

Abstract: A computer-implemented method, medium, and system are disclosed. One example computer-implemented method performed by a server includes obtaining training task information from a task party. The training task information includes information about a to-be-pretrained model and information about a to-be-trained target model. A respective task acceptance indication from each of at least one of a plurality of data parties is received to obtain a candidate data party set. The information about the to-be-pretrained model is sent to each data party in the candidate data party set. A respective pre-trained model of each data party is received. A respective performance parameter of the respective pre-trained model of each data party is obtained. One or more target data parties from the candidate data party set is determined. The information about the to-be-trained target model is sent to the one or more target data parties to obtain a target model.

Abstract: Embodiments of this specification provide a method and an apparatus for training a neural network model. The neural network model includes first neural network submodels located at first member devices. Each first member device uses private data to perform model prediction to obtain predicted label data and determines model update information of the first neural network submodel, and provides the model update information of the first neural network submodel and local sample distribution information to a second member device. The second member device performs neural network model reconstruction according to the model update information of the first neural network submodel of each first member device, determines an overall sample probability distribution according to the local sample distribution information of each first member device, and allocates the reconstructed neural network model and the overall sample probability distribution to each first member device.

Abstract: Embodiments of the present specification provide a method and an apparatus for clustering privacy data of a plurality of parties.

Abstract: Methods, systems, and apparatus for facial recognition. An example method includes storing encrypted facial features of a target user that are encrypted by using a first public key; receiving a recognition request from an end-user device of the target user, wherein the recognition request comprises an encrypted facial image and a second public key; performing homomorphic feature processing on the encrypted facial image; obtain obfuscated and encrypted output features; obtain obfuscated and encrypted facial features; transmitting the obfuscated and encrypted output features and the obfuscated and encrypted facial features to the end-user device; receiving from the end-user device a difference between the second intermediate value and the first intermediate value; and determining whether the to-be-recognized facial image corresponds to a facial image of the target user, comprising removing an impact of the first obfuscation and the second obfuscation on the difference.

Abstract: Methods, systems, and apparatus for facial recognition. An example method includes storing encrypted facial features of a target user that are encrypted by using a first public key; receiving a recognition request from an end-user device of the target user, wherein the recognition request comprises an encrypted facial image and a second public key; performing homomorphic feature processing on the encrypted facial image; obtain obfuscated and encrypted output features; obtain obfuscated and encrypted facial features; transmitting the obfuscated and encrypted output features and the obfuscated and encrypted facial features to the end-user device; receiving from the end-user device a difference between the second intermediate value and the first intermediate value; and determining whether the to-be-recognized facial image corresponds to a facial image of the target user, comprising removing an impact of the first obfuscation and the second obfuscation on the difference.

Abstract: Embodiments of the present specification provide a method and an apparatus for clustering privacy data of a plurality of parties.