Abstract: Provided is a graph data processing method, including: acquiring a degree of association of each node in a network graph; splitting the network graph to obtain a dense subgraph according to the degree of association of each node; determining stable nodes in the network graph and coreness of the stable nodes based on the dense subgraph, the coreness of the stable nodes being greater than a preset threshold; obtaining a sparse subgraph in the network graph according to the preset threshold, and remaining nodes in the network graph other than the stable nodes and connecting edges between the remaining nodes; and determining the coreness of each node in the sparse subgraph based on the sparse subgraph and the stable nodes.

Abstract: A method for federated data query includes: using, by a first electronic device, a joint query statement corresponding to a federated data query to obtain at least one operator containing a joint query operator; transforming the joint query operator to a first joint security operator; determining first data for intersection on the first electronic device by executing the first joint security operator, receiving second data for intersection from a second electronic device involved in the federated data query, and performing joint computing on the first data for intersection and the second data for intersection in a form of a ciphertext to obtain a joint data table; and determining a federated data query result corresponding to the at least one operator based on the joint data table.

Abstract: A federated learning method includes: determining at least one candidate feature from data features corresponding to a training data-set, the candidate feature corresponding to at least two decision trends in a decision tree model; obtaining n first decision tree models by taking the at least one candidate feature as a model construction foundation, value of n corresponding to number of the at least one candidate feature; determining at least one second decision tree model from the n first decision tree models based on prediction results of the n first decision tree models on training data in the training data-set; and transmitting the second decision tree model to a second computing device, the second computing device being configured to fuse at least two decision tree models that comprise the second decision tree model to obtain a federated learning model.

Abstract: Method for determining an extremum based on secure multi-party computation includes: acquiring a tth mean obtained in tth round federated computation; performing (t+1)th round federated computation based on valid node data of an ith node device in response to the valid node data of the ith node device being greater than the tth mean; performing the (t+1)th round federated computation based on invalid node data in response to the valid node data of the ith node device being less than or equal to the tth mean; and determining that the valid node data of the ith node device is the extremum in response to the valid node data of the ith node device being greater than or equal to an (n?1)th mean after (n?1)th round federated computation.

Abstract: A data processing method includes: acquiring a first intersection set, acquiring a second intersection set, calculating an intersection between the first intersection set and the second intersection set to obtain an intersection result set that includes an intersecting portion of the first intersection data and the second intersection data, and obfuscating the intersection result set to obtain an obfuscation set that includes obfuscated data based on data in the second intersection set and an intersection data set based on the intersection result set.

Abstract: This application provides a federated-learning-based data processing method, apparatus, and device, and a computer-readable storage medium. The method includes obtaining data to be processed, the data to be processed comprising multiple object identifiers and a feature value corresponding to each object identifier; binning the data to be processed based on the feature value corresponding to each object identifier to obtain a number of bins; determining multiple target identifier sets from each bin, and transmitting each target identifier set to a label-party device; receiving each piece of set label distribution information corresponding to each target identifier set from the label-party device, and determining bin label distribution information corresponding to each bin based on each piece of set label distribution information; and merging bins based on a binning policy and each piece of bin label distribution information to obtain a final binning result.

Abstract: A secure multi-party computation method and apparatus, a device, and a storage medium are provided, which belong to the field of data security technologies. The method includes: performing homomorphic encryption processing on first plaintext data to generate first ciphertext data; transmitting encrypted data containing the first ciphertext data to a second node device; receiving difference ciphertext data transmitted by the second node device; and decrypting the difference ciphertext data to obtain a positive and negative property of a difference between the first plaintext data and the second plaintext data. The foregoing method and apparatus, device, and storage medium are conducive to reducing the number of times of communications of secure multi-party computation, have low communication overhead and high computation efficiency, and enable magnitude comparison and equality testing to be performed simultaneously.

Abstract: A model training method and apparatus for federated learning, a device and a storage medium are provided, which belong to the technical field of machining learning. The method includes: generating an ith scalar operator based on a (t-1)th round of training data and a tth round of training data (201); transmitting an ith fusion operator to a next node device based on the ith scalar operator (202); determining an ith second-order gradient descent direction of an ith sub-model based on an acquired second-order gradient scalar, an ith model parameter and an ithfirst-order gradient; and updating the ith sub-model based on the ith second-order gradient descent direction to obtain a model parameter of the ith sub-model during a (t+1)th round of iterative training.

Abstract: Methods for training a federated model corresponding to the service system are provided herein. The method may include acquiring a first sample set associated with a first device in a service system, and a second sample set associated with a second device in the service system, wherein the service system comprises at least the first device and the second device. A virtual sample associated with the first device may be determined based on the first sample set; a sample set intersection may be determined based on the virtual sample and the second sample set. The method may include obtaining a training sample associated with the service system based on the sample set intersection, the first key set, and the second key set; and training a federated model corresponding to the service system based on the training sample.

Abstract: A graph data processing method includes acquiring a directed graph, where a directed edge in the directed graph is represented as pointing to a destination vertex from a start vertex; representing the directed edge in a unified form according to a specified order between a vertex identifier of the start vertex and a vertex identifier of the destination vertex; generating a tagged edge for recording an original edge direction of the directed edge, to obtain a tagged directed graph; and identifying a category of a triangle constituted by a vertex in the tagged directed graph, a neighbor vertex of the vertex, and a common vertex commonly adjacent to the vertex and the neighbor vertex, based on tagged edges between two of the vertex, the neighbor vertex, and the common vertex.

Abstract: A method, an apparatus, a computer device, a storage medium, and a program product for processing data are provided, which belong to the technical field of artificial intelligence. The method includes: acquiring model training information transmitted by each of at least two edge node devices, the model training information being transmitted in a form of plaintext, and being obtained by the edge node device by training sub-models through differential privacy; acquiring, based on the model training information transmitted by each of the at least two edge node devices, the sub-models trained by each of the at least two edge node devices; and performing, based on a target model ensemble policy, model ensemble on the sub-models trained by the at least two edge node devices, to obtain a global model. This solution expands the manner of model ensemble while ensuring the data security, thereby improving the model ensemble effect.

Abstract: A data processing method, apparatus, and device, and a storage medium are provided. The method is performed by a first device in a data processing system, and the method includes: obtaining first sample data of a target service; training a first service processing model based on the first sample data, to obtain a first model parameter of the first service processing model; transmitting, to a second device in the data processing system, the first model parameter, based on which and based on a second model parameter determined by the second device, a first fusion parameter is determined at the second device; obtaining a second fusion parameter, the second fusion parameter comprising model parameters respectively determined by at least three devices in the data processing system; and determining a target model parameter of the first service processing model based on the second fusion parameter.

Abstract: This application belongs to the technical field of artificial intelligence, and particularly relates to a data processing method, a data processing apparatus, a computer-readable medium, and an electronic device.

Abstract: This disclosure relates to a method for vertical federated learning. In multiple participation nodes deployed in a multi-way tree topology, an upper-layer participation node corresponds to k lower-layer participation nodes. After the upper-layer participation node and the k lower-layer participation nodes exchange public keys with each other, the upper-layer participation node performs secure two-party joint computation with the lower-layer participation nodes with a first public key and second public keys as encryption parameters to obtain k two-party joint outputs of a federated model. Further, the upper-layer participation node aggregates the k two-party joint outputs to obtain a first joint model output corresponding to the federated model. As such, a multi-way tree topology deployment-based vertical federated learning architecture is provided, improving the equality of each participation node in a vertical federated learning process.

Abstract: Embodiments of this application provide a training method, apparatus, and device for a federated neural network model, a computer program product, and a computer-readable storage medium.

Abstract: Provided is a graph data processing method, including: acquiring a degree of association of each node in a network graph; splitting the network graph to obtain a dense subgraph according to the degree of association of each node; determining stable nodes in the network graph and coreness of the stable nodes based on the dense subgraph, the coreness of the stable nodes being greater than a preset threshold; obtaining a sparse subgraph in the network graph according to the preset threshold, and remaining nodes in the network graph other than the stable nodes and connecting edges between the remaining nodes; and determining the coreness of each node in the sparse subgraph based on the sparse subgraph and the stable nodes.

Abstract: There is provided a method and a system for processing a communication channel. Including a heartbeat channel and a data channel between a master process and a worker process. The method includes determining at least one data channel associated with a heartbeat channel, detecting the determined at least one data channel, disconnecting the heartbeat channel when it is detected that any data channel is in a disconnected state to cause a heartbeat to time out, and ending a current task after it is determined that the heartbeat times out.

Abstract: A computer system resource allocation method is disclosed. The method may be implemented by a process and may comprise acquiring a total system resource and resource allocation data of each resource user submitting a resource application request, the resource allocation data including a maximum resource quota and a requested amount of resource by the each resource user, determining a corresponding resource saturation rate as a ratio between the amount of requested resource and the maximum resource quota, sorting the resource users according to the calculated resource saturation rates of the respective resource users, determining a boundary resource user using a set condition, according to the sorted resource users, and identifying resource users with a resource saturation rate smaller than or equal to that of the boundary resource user to be B-class resource users, and identifying resource users with a resource saturation rate larger than that of the boundary resource user to be C-class resource users.

Abstract: There is provided a method and a system for processing a communication channel. including a heartbeat channel and a data channel between a master process and a worker process. The method includes determining at least one data channel associated with a heartbeat channel, detecting the determined at least one data channel, disconnecting the heartbeat channel when it is detected that any data channel is in a disconnected state to cause a heartbeat to time out, and ending a current task after it is determined that the heartbeat times out.

Abstract: A computer system resource allocation method is disclosed. The method may be implemented by a process and may comprise acquiring a total system resource and resource allocation data of each resource user submitting a resource application request, the resource allocation data including a maximum resource quota and a requested amount of resource by the each resource user, determining a corresponding resource saturation rate as a ratio between the amount of requested resource and the maximum resource quota, sorting the resource users according to the calculated resource saturation rates of the respective resource users, determining a boundary resource user using a set condition, according to the sorted resource users, and identifying resource users with a resource saturation rate smaller than or equal to that of the boundary resource user to be B-class resource users, and identifying resource users with a resource saturation rate larger than that of the boundary resource user to be C-class resource users.