Abstract: This application provides a federated learning method, apparatus, and system, so that a server retrains a received model in a federated learning process to implement depersonalization processing to some extent, to obtain a model with higher output precision. The method includes: First, a first server receives information about at least one first model sent by at least one downstream device, where the at least one downstream device may include another server or a client connected to the first server; the first server trains the at least one first model to obtain at least one trained first model; and then the first server aggregates the at least one trained first model, and updates a locally stored second model by using an aggregation result, to obtain an updated second model.

Abstract: A first apparatus provides a second apparatus with encrypted label distribution information for the first node, so that the second apparatus calculates an intermediate parameter of a segmentation policy of the second apparatus side based on the encrypted label distribution information, and therefore a gain of the segmentation policy of the second apparatus side can be obtained. A preferred segmentation policy of the first node can also be obtained based on the gain of the segmentation policy of the second apparatus side and a gain of a segmentation policy of the first apparatus side. The encrypted label distribution information includes label data and distribution information, and is in a ciphertext state. The encrypted label distribution information can be used to determine the gain of the segmentation policy without leaking a distribution status of a sample set on the first node.

Abstract: Embodiments of this application provide a machine learning model update method, applied to the field of artificial intelligence. The method includes: A first apparatus generates a first intermediate result based on a first data subset. The first apparatus receives an encrypted second intermediate result sent by a second apparatus, where the second intermediate result is generated based on a second data subset corresponding to the second apparatus. The first apparatus obtains a first gradient of a first model, where the first gradient of the first model is generated based on the first intermediate result and the encrypted second intermediate result. After being decrypted by using a second private key, the first gradient of the first model is for updating the first model, where the second private key is a decryption key generated by the second apparatus for homomorphic encryption.

Abstract: This application discloses a model training method, and relates to the field of artificial intelligence. The method provided in this application is applicable to a machine learning system. The machine learning system includes a server and at least two client side devices. The method includes: A first client side device receives a first shared model sent by the server; outputs a first prediction result for a data set through the first shared model; obtains a first loss value based on the first prediction result; outputs a second prediction result for the data set through a first private model of the first client side device; obtains a second loss value based on the second prediction result; and performs second combination processing on the first loss value and the second loss value to obtain a third loss value, where the third loss value is used to update the first private model.

Abstract: A machine learning model training method is applied to a first client, a plurality of clients are communicatively connected to a server, the server stores a plurality of modules, and the plurality of modules are configured to construct at least two machine learning models. The method includes: obtaining a first machine learning model, where at least one first machine learning model is selected based on a data feature of a first training data set stored in the first client; performing a training operation on the at least one first machine learning model by using the first data set, to obtain at least one trained first machine learning model; and sending at least one updated module to the server, where the updated module is used by the server to update weight parameters of the stored modules.

Abstract: This application provides a data stream identification method and apparatus and belongs to the field of Internet technologies. The method includes: obtaining packet transmission attribute information of N consecutive packets in a target data stream; generating feature images of the packet transmission attribute information of the N consecutive packets based on the packet transmission attribute information of the N consecutive packets; and inputting the feature images into a pre-trained image classification model, to obtain a target application identifier corresponding to the target data stream. According to this application, accuracy of identifying an application identifier corresponding to a data stream can be improved.

Abstract: A method includes: A second node sends a prior distribution of a parameter in a federated model to at least one first node. After receiving the prior distribution of the parameter in the federated model, the at least one first node performs training based on the prior distribution of the parameter in the federated model and local training data of the first node, to obtain a posterior distribution of a parameter in a local model of the first node. After the local training ends, the at least one first node feeds back the posterior distribution of the parameter in the local model to the second node, so that the second node updates the prior distribution of the parameter in the federated model based on the posterior distribution of the parameter in the local model of the at least one first node.

Abstract: The technology of this application relates to a training method that includes a first terminal obtaining a to-be-trained first machine learning model from the server. The first terminal is any one of a plurality of terminals. The first terminal trains the first machine learning model by using local data stored by the first terminal, to obtain trained model parameters. The first terminal determines, based on a collaboration relationship, a first collaborative terminal corresponding to the first terminal, and sends a part or all of the trained model parameters of the first terminal to the server by using the first collaborative terminal. The collaboration relationship is delivered by the server to the first terminal. The foregoing manner can improve security of data exchange between the server and the terminal.

Abstract: A ground environment detection method and apparatus are disclosed, where the method includes: scanning a ground environment by using laser sounding signals having different operating wavelengths, receiving a reflected signal that is reflected back by the ground environment, determining scanning spot information of each scanning spot of the ground environment based on the reflected signal, determining space coordinate information and a laser reflection feature of each scanning spot based on each piece of scanning spot information, partitioning the ground environment into sub-regions having different laser reflection features, and determining a ground environment type of each sub-region.

Abstract: The method and apparatus that are applied to a machine learning system which includes at least one parameter collection group and at least one parameter delivery group. Each parameter collection group is corresponding to at least one parameter delivery group. The method includes: when any parameter collection group meets an intra-group combination condition, combining model parameters of M nodes in the parameter collection group to obtain a first model parameter of the parameter collection group, where a smallest quantity s of combination nodes in the parameter collection group?M?a total quantity of nodes included in the parameter collection group; and sending the first model parameter of the parameter collection group to N nodes in a parameter delivery group corresponding to the parameter collection group, where 1?N?a total quantity of nodes included in the parameter delivery group corresponding to the parameter collection group.

Abstract: Embodiments of the present invention provide a model parameter fusion method and apparatus, which relate to the field of machine learning and intend to reduce a data transmission amount and implement dynamical adjustment of computing resources during model parameter fusion. The method includes: dividing, by an ith node, a model parameter of the ith node into N blocks, where the ith node is any node of N nodes that participate in a fusion, and 1?i?N?M; receiving, by the ith node, ith model parameter blocks respectively sent by other nodes of the N nodes than the ith node; fusing, by the ith node, an ith model parameter block of the ith node and the ith model parameter blocks respectively sent by the other nodes, so as to obtain the ith general model parameter block; and distributing, by the ith node, the ith general model parameter block to the other nodes of the N nodes.

Abstract: A receiver or method uses an offset vector to provide seamless switching between a real-time kinematic (RTK) mode and a precise positioning mode (e.g., precise point positioning, PPP) mode. An offset module or data processor is arranged to determine an offset between precise position and the RTK position estimate. Upon loss of the RTK signal, switching to a precise position mode based a last available RTK position (e.g., if the precise position mode is converged on a position solution with resolved ambiguities of the carrier phase), wherein the next precise position estimate is compensated by the offset or reference frame bias to avoid a jump or discontinuity in the next precise position estimate.

Abstract: This application provides a data stream identification method and apparatus and belongs to the field of Internet technologies. The method includes: obtaining packet transmission attribute information of N consecutive packets in a target data stream; generating feature images of the packet transmission attribute information of the N consecutive packets based on the packet transmission attribute information of the N consecutive packets; and inputting the feature images into a pre-trained image classification model, to obtain a target application identifier corresponding to the target data stream. According to this application, accuracy of identifying an application identifier corresponding to a data stream can be improved.

Abstract: A system or method uses an offset vector to provide seamless switching between a real- time kinematic (RTK) mode and a precise positioning mode. A correction wireless device is adapted to receive, at the reference receiver, a precise signal encoded with precise correction data. A precise positioning estimator of the reference receiver is arranged to determine a precise position based on the measured carrier phase of the received satellite signals and the received precise correction data in a precise correction mode. At the reference receiver, an offset module can determine a base offset vector between the precise position and a reference RTK position for the reference receiver. At the reference receiver, a wireless communications device is capable of transmitting, via an RTK signal, RTK correction data.

Abstract: The method includes: receiving, by an obstacle information processing device (110), description information of n obstacles collected by an obstacle detection data collector (120) for a target transport means, and updating an obstacle information list based on the description information of the n obstacles, to obtain an updated obstacle information list; obtaining, by the obstacle information processing device (110), target area information and non-target area information based on the updated obstacle information list, and sending the target area information and the non-target area information to a laser radar data collector (130); receiving, by the obstacle information processing device (110), a sampling point set collected by the laser radar data collector (130); and finally obtaining, by the obstacle information processing device (110), an obstacle detection result based on the sampling point set and the updated obstacle information list.

Abstract: An offset module or navigation positioning estimator determines a reference frame bias between precise point positioning (PPP) reference frame and an RTK reference frame, where the PPP reference frame is associated with relative position estimates generated by the relative position estimator and where the RTK reference frame is associated RTK position estimates generated by the RTK position estimator. Upon loss of the RTK correction signal, the navigation positioning estimator or controller switches to a relative position mode based a last available RTK position. The relative position estimator determines an estimated relative position based on time-differenced phase measurements by the mobile receiver in the relative position mode. The relative position estimator or offset module offsets the estimated relative position in the relative position mode.

Abstract: A ground environment detection method and apparatus are disclosed, where the method includes: scanning a ground environment by using laser sounding signals having different operating wavelengths, receiving a reflected signal that is reflected back by the ground environment, determining scanning spot information of each scanning spot of the ground environment based on the reflected signal, determining space coordinate information and a laser reflection feature of each scanning spot based on each piece of scanning spot information, partitioning the ground environment into sub-regions having different laser reflection features, and determining a ground environment type of each sub-region.

Abstract: Embodiments of the present invention provide a model, which relate to the field of machine learning and intend to reduce a data transmission amount and implement dynamical adjustment of computing resources during model parameter fusion. The method includes: dividing, by an ith node, a model parameter of the ith node into N blocks, where the ith node is any node of N nodes that participate in a fusion, and 1?i?N?M; receiving, by the ith node, ith model parameter blocks respectively sent by other nodes of the N nodes than the ith node; fusing, by the ith node, an ith model parameter block of the ith node and the ith model parameter blocks respectively sent by the other nodes, so as to obtain the ith general model parameter block; and distributing, by the ith node, the ith general model parameter block to the other nodes of the N nodes.

Abstract: The method and apparatus that are applied to a machine learning system which includes at least one parameter collection group and at least one parameter delivery group. Each parameter collection group is corresponding to at least one parameter delivery group. The method includes: when any parameter collection group meets an intra-group combination condition, combining model parameters of M nodes in the parameter collection group to obtain a first model parameter of the parameter collection group, where a smallest quantity s of combination nodes in the parameter collection group?M?a total quantity of nodes included in the parameter collection group; and sending the first model parameter of the parameter collection group to N nodes in a parameter delivery group corresponding to the parameter collection group, where 1?N?a total quantity of nodes included in the parameter delivery group corresponding to the parameter collection group.

Abstract: In a system for navigating a moving object according to signals received from satellites, a moving object receives mobile base data from a mobile base station, the received mobile base data including satellite measurement data of the mobile base station, the satellite measurement data of the mobile base station including code measurements and carrier phase measurements for the plurality of satellites, and position-related information of the mobile base station. In accordance with the satellite navigation data for the moving object and the received mobile base data, the moving object performing a real-time kinematic (RTK) computation process to resolve carrier phase ambiguities and determine a relative position of the moving object relative to the mobile base station. A signal reporting information corresponding to the relative position is sent via a transmitter of the moving object.