789 results about "Client-side" patented technology

The invention discloses a dynamic weight aggregation federated learning method and system for credit risk assessment, and the method comprises the steps: firstly, calculating the cosine similarity of each client model and the last gradient of a global model, so as to assess the consistency of model parameters; secondly, the distribution difference is quantified by calculating the data statistic difference between each client data and the global data, so that the diversity of the data is evaluated; further, a contribution degree comprehensively considering model parameter similarity and data distribution difference is obtained by combining a hyper-parameter with cosine similarity and data distribution difference; and finally, dynamically adjusting the weight of each client in global model updating according to the contribution degree of each client, so that the model pays more attention to the client with higher data quality, thereby improving the overall model performance. The federated learning method is especially suitable for processing non-independent identically distributed data, optimizes the generalization ability of the model, and is suitable for credit risk assessment in the financial field.

The invention provides a grouping federal recommendation method based on bilateral additive article embedding. A central server constructs a double-layer article embedding characterization structure under a federated learning framework: a client locally maintains user personalized embedding and an article local personalized embedding matrix, and a server generates global group shared article embedding through a dynamic clustering grouping mechanism; superposing global sharing embedding and local personalized embedding by adopting an additive fusion strategy to generate user side personalized article characterization; a progressive course learning scheme is designed, and smooth transition from complete personalization to additive representation is realized by dynamically adjusting a regularization weight coefficient; and a grouping and clustering process is optimized in combination with a knowledge migration strategy, and collaborative knowledge sharing across user groups is promoted. In a client local training stage, a personalized recommendation loss function based on binary cross entropy is constructed, global shared embedding and local embedding parameters are synchronously updated, and a server side updates a global model through grouping federation aggregation. On the premise of protecting user privacy, the problems that in traditional federated recommendation, article characterization is simplified, and personalized perception is insufficient are effectively solved, the accuracy of a recommendation system is remarkably improved, communication overhead is reduced, and the method is suitable for personalized recommendation services of privacy sensitive scenes such as e-commerce and content platforms.

The invention discloses a federated learning method based on quantum transfer learning, relates to the cross technical field of quantum computing and federated learning, and aims to solve the problem of performance bottleneck of traditional federated learning under non-independent identically distributed data. The local model of each client is fused with a classic convolutional layer and a quantum convolutional layer; during local training, firstly, classic features are extracted by a classic convolutional layer, and then the classic features are coded and input into a quantum convolutional layer to generate quantum features; and the two features are spliced and then classified, and model parameters are uploaded. And the server side adopts a FedAvg algorithm to aggregate parameters so as to update the global model. According to the method, a transfer learning mechanism is introduced to optimize the initialization of the quantum layer, and an experimental result shows that the classification performance and robustness of the method are remarkably superior to those of a traditional federated CNN model on a non-IID image data set, and the method is particularly suitable for a privacy protection cooperative computing scene.

The invention relates to the technical field of industrial Internet of Things and wireless sensor networks, in particular to a large-scale industrial fault diagnosis system and method based on Bluetooth MESH, and efficient monitoring and fault early warning of industrial equipment are realized through low-power-consumption sensor nodes, edge computing, a Bluetooth Mesh network and a clustered Mesh tree architecture. The system comprises a client, a server, a gateway, a cluster head and a cluster node, the cluster node comprises a sensor node and a relay node, a lightweight neural network model is built in the sensor node, and edge reasoning and fault detection can be realized locally. Stable communication and real-time cooperation of large-scale nodes are ensured through a decentralized architecture of Bluetooth Mesh, establishment of a dynamic cluster head chain and optimization of an RSSI threshold value. And a low-power-consumption mechanism combining event driving and fixed polling is adopted, so that the energy consumption of the system is remarkably reduced. In-cluster communication loads are reduced through a clustering architecture, and the communication efficiency and the anti-interference capability of the system are improved in combination with channel separation of Mesh and BLE protocols.

The invention discloses a virtualization hardware verification method and verification system based on a multi-layer architecture, the multi-layer architecture sequentially comprises a first module, an exchange module and a second module, the first module is a client side, and the second module is a simulation side; the verification method comprises the following steps: starting and simulating virtual hardware equipment in a first module by adopting a virtualization platform, and generating hardware behavior data; transmitting the hardware behavior data to the exchange module; the switching module receives the hardware behavior data from the first module and processes the hardware behavior data to obtain target data meeting the requirement of the second module; the processing is to convert the language format of the hardware behavior data generated by the first module into a target language format adaptive to a second module based on a hardware event flow mechanism; the target data is transmitted to the second module, and the second module receives the target data and executes simulation of hardware behaviors based on the target data.

The invention relates to the technical field of dialogue information analysis, and discloses an emotion recognition method and system based on a federal multimodal global diffusion model, a terminal and a storage medium, and the method comprises the steps: capturing a dialogue dependency relationship through a dialogue graph network, and carrying out the forced semantic alignment through a semantic condition network; consistency of the recovery modality and the available modality in context, speaker identity and semantics is ensured, and the recovery module and the classifier module are periodically frozen to facilitate federal collaborative optimization. According to the method, federal learning and generative mode recovery are combined, so that the problem of unpredictable mode missing of the client in a multi-mode dialogue emotion recognition scene is solved, and the emotion recognition accuracy is improved.

The invention provides a communication segmentation learning system and method for adaptive channel compression, and a medium, and relates to the technical field of segmentation learning and communication compression. The system comprises a plurality of clients, a server side and a channel compression device arranged between the clients and the server side, the channel compression device comprises a channel sensitivity modeling module, a rate distortion adaptive compression module and a cross-client fair coordination module. In a channel sensitivity modeling module, channel importance is dynamically evaluated through intermediate layer activation value fusion; differentiated quantization and compression strategies are designed on the basis of sensitivity scores in a rate-distortion self-adaptive compression module, so that key information is reserved while communication overhead is reduced; and meanwhile, the cross-client fair coordination module realizes balanced distribution of communication resources among multiple clients through a fairness regularization and dual optimization mechanism, so that the influence of excessive compression on global convergence is avoided. According to the method, the communication efficiency and the training stability of segmentation learning in a complex heterogeneous environment are remarkably improved.

The invention discloses a cross-institution financial data federated learning modeling system and a privacy compliance verification method, and relates to the technical field of financial data processing, the system comprises a federated aggregation module used for a server to align financial time sequence characteristics of each institution through a timestamp hash alignment strategy, and a privacy verification module used for verifying the privacy compliance of each institution; aggregating the aligned financial time sequence characteristics by using a federated time convolutional network, and generating time sequence model parameters for capturing cross-mechanism global time sequence dependence; the federation reasoning module is used for generating a minimum spanning tree representation of a global enterprise association relationship based on a security multi-party computing strategy by the server side on the basis of a local sub-graph of the enterprise association relationship constructed by each institution client side; the server performs cross-mechanism graph reasoning by using a federated graph attention network and an aggregation graph embedding vector and combining minimum spanning tree representation, and constructs graph model parameters for identifying enterprise associated risks; and the model joint training module is used for the server to fuse the time sequence model parameters and the graph model parameters so as to determine global model parameters.

The invention discloses a WebSocket-based instant message task assigning system. The system comprises two parts, i.e., a server and clients, wherein the server is deployed on an application server and configured with ports for providing external services, and the server is then started; the clients comprises an APP client and a PC client; and information data transmission among the server, the APP client and the PC client is achieved based on the WebSocket protocol, that is, the server provides the instant messaging service, and the clients achieve acquisition of data from the server. Compared with the prior art, the system provided by the invention has the advantages that WebSocket-based instant message task assigning is achieved, and information can be synchronized between the information acquisition clients, so that high timeliness, effectiveness and safety of the information are ensured.

The invention discloses an unmanned aerial vehicle group real-time image recognition method and system based on asynchronous federated learning, and the method comprises the steps: carrying out the local recognition model training of each unmanned aerial vehicle, and uploading a trained local model to a central server; calculating a corresponding Shapley value, and introducing an old degree attenuation factor to adjust the weight to obtain an adjusted Shapley value; updating the global model through weighted aggregation based on the adjusted Shapril value; and after the global model is updated, the unmanned aerial vehicle with the highest utility is selected by adopting a confidence upper limit algorithm to participate in subsequent federated learning, and client optimization scheduling in a dynamic environment is realized. The asynchronous federated learning mechanism based on the Shapley value can effectively measure the contribution of each unmanned aerial vehicle to the global model, reduces the influence of outdated updating by introducing an attenuation factor, improves the accuracy of the global model in a dynamic environment, enables the model to better adapt to the complex environment and data change of the unmanned aerial vehicle group, and improves the robustness of the unmanned aerial vehicle group. Therefore, the accuracy of image recognition is improved.

The invention discloses a drug molecule generation method capable of explaining a federal large model, and relates to the technical field of artificial intelligence, and the method comprises the steps: S1, through a data feature extraction and model initialization step, extracting multi-modal features from distributed data, constructing and initializing a drug molecule generation model, and obtaining a drug molecule generation model; s2, performing parameter optimization on a federal learning client through the large model, including local training fine tuning based on a distributed client, and integrating fine-tuned parameters into a global model of a server to improve model performance and data utilization efficiency, S3, based on the optimized model, generating drug molecular structure information including a molecular description sequence and a molecular graph; according to the drug molecule generation method capable of explaining the federal large model, on the basis of guaranteeing data privacy, the generation capacity and diversity of new molecules are remarkably improved, meanwhile, the transparency and credibility of the model are enhanced through causal analysis and an attention mechanism, the performance and robustness of the model are improved, and the method has wide application prospects.

The invention discloses a short video automatic generation method and device based on a new media content creation model, and a server side method comprises the steps: receiving a short video generation request sent by a client side, wherein the short video generation request carries a video production description text and a user identifier; inputting the video production description text into a pre-fine-tuned large language model, and outputting video production scheme information; searching temporary video materials and system materials uploaded by the user from a BI system through the user identifier; under the condition that the temporary video material is not empty, inputting the video production scheme information, the temporary video material and the system material into a video generation model; or under the condition that the temporary video material is empty, inputting the video production scheme information and the system material into the video generation model; and outputting the target short video, and sending the target short video to the client. Therefore, by adopting the embodiment of the invention, the generated short video can show rich layering sense and emotion tension, so that the quality of the short video is improved.

The present application relates to the field of federated learning and privacy protection, and particularly relates to a direction-aware local differential privacy federated learning method. The present application effectively solves the problem of serious decline in model performance caused by the destruction of gradient direction information by traditional differential privacy methods by adopting a direction-aware noise selection strategy based on an exponential mechanism locally on the client side. Specifically, the client first performs L2 norm clipping on the gradient to control the sensitivity, then generates K candidate noise vectors from a Gaussian distribution, evaluates the direction consistency of each candidate noise and the true gradient through cosine similarity, and selects the noise closest to the gradient direction in a probabilistic manner using the exponential mechanism, and finally adds the selected noise to the gradient to complete the perturbation. The method provides a strict epsilon-local differential privacy mathematical proof for the entire noise selection process, reduces the calculation time by about 80% compared to existing direction-aware methods, and achieves a triple balance of privacy protection, model utility and computational efficiency.

The invention relates to the technical field of fault diagnosis, in particular to a fault diagnosis model construction method based on distributed causal discovery and federated learning. According to the method, a covariance tensor containing statistical association information of an observation variable and an agent variable is obtained, a global causal graph is obtained by combining a federal causal discovery method introducing the agent variable, then a converted causal intensity matrix is embedded into a graph convolutional neural network, and finally a personalized and global diagnosis model is trained by using a FedAvg framework fused with a Dito algorithm. According to the method, distributed heterogeneous data can be effectively processed while data privacy is protected, model interpretability is improved by mining a causal relationship between variables, global generalization and client personality requirements are considered, the accuracy and generalization ability of fault diagnosis are remarkably improved, and the fault diagnosis efficiency is improved. The method is especially suitable for fault diagnosis of industrial bearings and other scenes needing dispersed sensitive data processing.

The invention discloses an intelligent beef cattle breeding monitoring management system based on whole industry chain data. The system comprises a data acquisition module which is used for deploying sensors and data acquisition terminals in each link of a beef cattle whole industry chain for data acquisition; the data transmission and integration module is used for transmitting data acquired in each link to a cloud data center for processing by using a wireless communication technology; the intelligent analysis module is used for mining the integrated data and adjusting breeding processes such as seed selection and matching, a nutrition formula and a management strategy; the monitoring and early warning module is used for setting a key index threshold value, and when the key index exceeds the threshold value, the system automatically sends out early warning information to a mobile phone terminal or a computer client of a culture worker; and the breeding decision support module is used for providing guidance for the breeding direction according to long-term accumulated data and analysis results and screening out good beef cattle individuals and families thereof. The system has the advantages of high breeding efficiency, high beef cattle variety improvement speed and the like.

The invention discloses a temperature control method of a board card, a program product, electronic equipment and a storage medium, and relates to the technical field of big data algorithms, and the method comprises the steps: obtaining the current temperature of the board card collected by a plurality of temperature sensors in a working state to determine the actual temperature of the board card, and calculating the difference value between the current temperature and a preset temperature; and based on a comparison result of the difference value and a preset safety threshold value, generating a target adjusting impedance of a heating circuit of the board card or a target wind speed of a fan so as to adjust a variable resistor of the heating circuit or the wind speed of the fan, so that the actual temperature is in a preset safety interval. The technical problems that due to the fact that one path of sensor of board card design is abnormal, the whole board card works abnormally, user service interruption is caused, board card cost and maintenance cost are increased, and manpower and material resources are wasted are solved, and the purposes of dynamically adjusting board card impedance, heating circuit power and fan rotating speed according to the environment temperature to ensure normal work of the board card and improving the service life of the board card are achieved. And the technical effect that the service interruption of the client is avoided is ensured.

An RFID transponder including a Bluetooth® compatible transceiver (BLEET) is described. The Bluetooth® compatible transceiver may be configured to set data that is transmitted via one or more RFID transceivers in the transponder and to return data received by the RFID transceiver(s) to a client application running, for example, on a user's smart phone. The BLEET may be used for electronic vehicle tracking or tolling. Vehicle occupancy data may be set by the user with the client application via a Bluetooth® connection in connection with high occupancy vehicle tolling and express lane incentive programs. The BLEET may be used in a congestion pricing (CP) toll area, wherein the smart phone determines a toll rate based on the location, when triggered by the BLEET, that the BLEET was read by an RFID reader located on a boundary of the CP toll area.

The invention provides a segmentation federal learning method and system based on aggregation gradient broadcast, and the method comprises the steps: initializing a global model, and segmenting the global model into a server model and a client model according to the capability limitation of a local device; issuing the client model to all local equipment terminals; the multiple local devices execute forward propagation at the same time, and shredded data are obtained through calculation; transmitting the shredded data and the training data label to an edge server; performing forward propagation in parallel according to the shredded data, calculating the loss of each local equipment end in combination with a corresponding training data label, and performing back propagation to obtain a loss function and a gradient of the shredded data; aggregating the gradient of the shredded data and broadcasting to all local equipment; updating the server side model and the client side model, and aggregating the server side model; and the iterative learning loop is repeatedly executed until convergence or the maximum communication round is reached. According to the invention, the communication overhead in segmentation federal learning is saved, and the model training efficiency is effectively improved.

The subject technology receives an input image, the input image comprising a selfie. The subject technology transforms, using a neural network, the input image to a latent representation of an identity. The subject technology transforms, using a diffusion model, a text condition to a second latent representation compatible with the latent representation of the identity. The subject technology transforms a pose template to a set of latent features for the diffusion model. The subject technology generates an intermediate image based on the latent representation of the identity, the second latent representation, and the set of latent features. The subject technology modifies, using a face enhancement network, the intermediate image based on the input image. The subject technology generates, using a face restoration network, a final output image based on the modified intermediate image. The subject technology provides for display the final output image on a display of a client device.

The invention discloses a federated learning-based large language model fine tuning method, apparatus and device, and a medium, relates to the technical field of federated learning, is applied to a server, and comprises the steps of obtaining a federated fine tuning task, and processing data in the federated fine tuning task by using a preset experience guidance model to obtain corresponding feature vectors; issuing the feature vector, a preset experience guidance model and a basic large language model to each client, so that each client performs model fine tuning processing on the basic large language model based on the local fine tuning data set, the feature vector and the preset experience guidance model to obtain a fine-tuned large language model, and performing aggregation processing on the fine-tuned large language model to obtain a new feature vector, and then skipping to the step of issuing the feature vector, the preset experience guidance model and the basic large language model to each client until a preset fine-tuning training ending condition is met. In this way, fine tuning of the large language model is achieved through personalized federal learning.

The invention discloses a multipath load control acquisition device and system and a load control acquisition method, the device adopts an architecture in which a master device and a slave device cooperate, the master device comprises a master controller, a master communication module, a user display interface and a master interface unit, and the slave device comprises a slave interface unit, a slave controller, an acquisition unit and a slave communication module. Flexible marshalling management is achieved by distributing electronic identity numbers to the breakers, load parameters and the states of the breakers are synchronously collected through the collection unit, a two-way data transmission channel is constructed by means of the master-slave communication module, a control signal is generated based on preset logic and real-time data through the master controller, and the state of the breakers is controlled. And the slave controller issues an instruction to drive the circuit breaker electric operation mechanism to realize millisecond-level rapid switching-on and switching-off operation. According to the invention, the problems of insufficient number of loops, lack of state monitoring, slow response and the like of an existing load control system are solved, and accurate, rapid and intelligent management of multi-path loads on a client side is realized.

Systems and methods herein are provided for delivering personalized trivia content in an interactive game using tangible game elements and a digital companion application. Players are enabled to select and scan multiple tangible game elements (e.g., via quick-response codes, ArUco markers, tags, or other identifiers), each representing a game parameter value (e.g., a topic or category), through a client-side user interface of the digital application to create different combinations. The user interface is coupled to a backend host that controls communications between the user interface and a database storing cache records including game content for particular combinations. The backend host queries the database to check for available game content for the selected combination (subject to certain constraints, such as game content not already previously presented to a user). If not, the backend host transmits a query to a generative artificial intelligence model trained to generate new game content.

The invention relates to a specific driver gene and common driver gene identification method based on federal transfer learning and deep learning algorithms, and the method comprises the steps: constructing a data set based on data of different cancer types; based on a multi-head attention mechanism, preprocessing the data set; based on the preprocessed data set, training a neural network model to obtain a gene recognition model; wherein the neural network model is constructed based on a Chebyshev graph convolutional network and a graph convolutional network, model training adopts a federated transfer learning method, and the training process is divided into a server side and a client side; based on the gene recognition model, cancer specificity and common driver genes across a tumor are identified. Compared with the existing method, the method provided by the invention can more accurately and efficiently identify the specific and common driver genes across tumors.

The invention provides a SaaS service security verification method and system under a zero-trust architecture. The method comprises the following steps: a client sends a login request to a server; the server side verifies the login request and returns a dynamic key, a coding rule table and a shuffling rule to the client side based on the login request and a current time window; the client generates a first password table corresponding to the dynamic key, hashing data of the service request according to a coding rule table, embedding the data into the first password table, performing shuffling processing of various different schemes on the first password table according to a shuffling rule to obtain a client request signature, and sending the service request and the client request signature to the server; and the server side obtains the server side request signature according to the same logic, verifies the client side request signature based on the server side request signature, and returns a response result corresponding to the service request to the client side after the verification is passed. According to the method, the end-to-end data integrity and security in the communication process of the client and the server can be remarkably improved.

The invention discloses a heterogeneous data efficient federated learning method for intelligent manufacturing fault diagnosis, and the method comprises the steps: carrying out the two-stage federated learning based on a multi-agent generative adversarial network MAD-GAN frame for the fault detection of a bearing vibration signal; in the first stage, the MAD-GAN model is trained in the federated learning environment, the data sample expansion process can effectively relieve the modal collapse problem, and in the second stage, the federated MAD-GAN model is utilized to generate data samples with high quality and diversity for a client, so that federated learning model training is performed, and the generalization ability and diagnosis precision of the model are improved. In order to further reduce communication overhead, an advanced model compression technology is introduced in the second stage of the invention, and a sparse method and a quantization method such as Top-k are combined. Redundancy in the communication process can be eliminated, the data transmission quantity is reduced, the problem of non-independent identically distributed data in industrial fault diagnosis can be solved, and the communication efficiency is remarkably improved.

The invention discloses a medical image data privacy protection method and system in a federated learning mode, and the method comprises the following steps: training an original federated learning model through a gradient descent method based on local medical image data by each client as a local model, calculating the training gradient information of the local model and the statistical magnitude of the local medical image data, uploading the statistics of the local medical image data and the local model training gradient information to a server side; the server side receives the statistics and the information uploaded by each client side to generate a disturbance model; each client receives the disturbance model and updates local model parameters; an attacker trains the substitution model based on a generalization error-oriented confrontation optimization mechanism, and the server side updates generator parameters according to the attack situation and defense feedback of the substitution model to optimize defense; and through multi-round training of the substitution model by an attacker and optimization defense of the server side, the defense effect of the federated learning model for resisting the attack of the substitution model is evaluated, and if an expected effect is not achieved, training continues.

The invention discloses a real-time software deformation interaction method based on a physical engine in an environment. The real-time software deformation interaction method comprises the following steps: S1, establishing a virtual environment and a software model; s2, constructing an interactive perception and collision detection network; s3, receiving and analyzing a multi-modal interaction instruction; s4, real-time deformation calculation based on a physical engine; s5, performing multi-user collaborative state synchronization and rendering feedback; and S6, carrying out fault state simulation on the software object. According to the method, real-time and high-precision simulation of software deformation is realized through deep fusion of software dynamics calculation of a physical engine and an interaction instruction stream, and through hierarchical physical attribute binding and client prediction, the network synchronization data volume is greatly reduced while the millimeter-level deformation precision is ensured, so that the network synchronization efficiency is improved. And low-delay and high-consistency experience under multi-person cooperative operation is ensured.

The invention discloses a longitudinal federated learning privacy protection method and system considering data leakage between federated friends, and belongs to the field of vertical federated learning privacy protection. The method comprises the following steps: recording an initial accuracy rate of a server top layer model of a longitudinal federated learning system, and maintaining a data subset sampled from local data of each client by a server; each client is regarded as a second client in sequence, the other clients are regarded as first clients, risks of stealing data of the second clients by the first clients are calculated, the maximum risk value is regarded as the overall privacy disclosure risk of the second clients, and if the overall privacy disclosure risk exceeds a risk threshold value, the second clients are regarded as the first clients. If yes, adding noise to the update gradient issued to the second client by the server, and recalculating the overall privacy disclosure risk based on a noise adding result until the privacy protection condition is met; traversing all the clients; according to the method, the problem of privacy protection capability enhancement oriented to the risk of data leakage among federates independent of labels, intermediate results and top models in longitudinal federated learning is solved.

The invention discloses a federated learning participant selection method and system, and the method comprises the steps: obtaining local model parameters uploaded by all federated learning client participants through a central server, and constructing a local model feature observation tensor of each client participant; calculating kernel similarity between participants of the client; determining a positive semi-definite similarity kernel matrix; the central server complements missing values of the positive semi-definite similarity kernel matrix to obtain a complete similarity kernel matrix; the central server constructs a timeliness weight diagonal matrix; the central server constructs a likelihood matrix according to the complete similarity kernel matrix and the timeliness weight diagonal matrix; solving the likelihood matrix through a determinant point process to obtain a selected client participant subset; averaging all local model parameters in the selected client participant subset to obtain a global model; and finally, issuing the global model to the selected client participant.

The invention discloses a power distribution line passive real-time fault positioning system, which belongs to the technical field of power distribution line fault detection and comprises a power distribution line fault positioning device management system and a plurality of fault positioning terminals distributed along a power distribution line. The distribution line fault positioning device management system establishes two-way communication with each fault positioning terminal through a 4G / 5G wireless public network; the distribution line fault positioning device management system comprises a data center, a client and a plurality of mobile terminals. The fault positioning terminal comprises an ARM processor, a time synchronization unit, a signal acquisition unit, a signal processing unit, a passive self-powered unit, a communication unit and an auxiliary unit. According to the invention, the distributed fault positioning terminal is combined with an intelligent algorithm, so that high-precision fault recognition and real-time positioning under a complex network are realized, and the fault handling efficiency of the power distribution network is remarkably improved.