438 results about "Client data" patented technology

A system and method are provided for providing advisory notifications to mobile applications. The method includes interfacing the server device with at least one endpoint within an enterprise system and storing a model trained by a machine learning engine to automatically determine advisory notifications relevant to client data sets stored by the endpoint(s) and / or the at least one endpoint. The method also includes determining a current state of a client account, using the model to determine an advisory notification for the client account based on the current state, referring to a set of rules to determine when to provide the advisory notification in the mobile application, and in what portion of the mobile application to display the notification; and sending the advisory notification via the communications module to a client device to display the advisory notification in the mobile application.

A system and method are provided for providing advisory notifications to mobile applications. The method includes interfacing the server device with at least one endpoint within an enterprise system and storing a model trained by a machine learning engine to automatically determine advisory notifications relevant to client data sets stored by the endpoint(s) and / or the at least one endpoint. The method also includes determining a current state of a client account, using the model to determine an advisory notification for the client account based on the current state, referring to a set of rules to determine when to provide the advisory notification in the mobile application, and in what portion of the mobile application to display the notification; and sending the advisory notification via the communications module to a client device to display the advisory notification in the mobile application.

A method for enhancing agent-customer interactions in a digital engagement service is provided. The method includes receiving a customer's communication request and initiating an asynchronous process to generate a customer profile summary using a customer identifier. This involves querying a customer data platform (CDP) for customer traits and event data, and creating a prompt for a large language model (LLM) to produce a concise customer profile summary. The summary, stored in a data store, is presented to an available agent through a user interface alongside an invitation to accept the incoming communication request. This streamlined approach equips agents with relevant customer insights promptly, improving service quality and response times.

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 discloses a federal forgetting learning method based on small sample knowledge distillation. The method comprises the following steps: initializing a global model by adopting a server and broadcasting the global model to each client; each client carries out local training on the received global model by using local data, and after the local training is completed, the global model is updated and sent back to the server; the server stores and aggregates the global models sent by all the clients, is used for updating the local global model of the server, and repeats the federal learning process from S1 to S3; a target client makes a forgetting request at a certain moment, federal learning is stopped, and at the moment, a server side uses a forgetting module to generate a forgetting model and takes the forgetting model as a student model; the server side finely adjusts and compares the language image pre-training model according to local small sample data, so that the language image pre-training model adapts to downstream data and serves as a teacher model; the server uses local small sample data to carry out recovery training through a knowledge distillation method, so that catastrophic forgetting caused by client data revocation is avoided, and the performance of a forgetting model is improved.

System, methods, apparatuses, and computer program products are disclosed for network policy generation using a fine-tuned large language model (LLM). Client data associated with a user device is determined and provided to the fine-tuned LLM in a prompt requesting a network policy. Client data may include device information, network information, application information, and connection information. The fine-tuned LLM generates a network policy based on the client information. The generated network policy is provided to the client to enable the client to enforce the network policy on the user device.

The invention provides a mask-masked transverse federal heterogeneous graph learning method and device. The method comprises two stages of client local training and global aggregation. The client deploys a local model based on the initial parameters distributed by the global server, shades random shading input features by using dynamically generated masks to mine a high-order topological structure, and optimizes the topology perception capability through edge reconstruction loss; meanwhile, node attributes are randomly masked, the masking proportion is dynamically adjusted, and semantic robustness is enhanced in combination with consistency semantic loss; and jointly updating local parameters by combining the node classification loss. In the global aggregation stage, the server calculates a clustering coefficient to measure the compactness of a graph structure according to an edge set under an appointed meta path of each client, dynamically distributes aggregation weights in combination with the number of nodes, fuses client parameters rich in topological information, and iteratively optimizes a global model. According to the method, the problem of semantic inconsistency caused by dynamic change of client data can be solved, the local graph structure value is captured, and the global generalization ability and convergence efficiency are improved.

The invention provides a federated learning backdoor defense method based on singular value decomposition and model weight amplification, and the method comprises the steps: carrying out the normalization processing of a model updating parameter locally trained by a client, and obtaining a normalized model updating parameter; on the basis of the normalized model updating parameters, model updating parameters after dimension reduction are obtained; performing clustering algorithm processing on the model updating parameters after dimension reduction to obtain clustered clusters; obtaining cluster model parameters based on the clustered clusters; combining the cluster model parameters into a cluster model parameter matrix; performing singular value decomposition on the cluster model parameter matrix to obtain a singular vector; obtaining a trust score through the singular vector; obtaining global model update parameters based on the trust score; calculating by utilizing the global model updating parameters to obtain a global model; and adding Gaussian noise to the global model through a differential privacy mechanism to obtain a final global model. According to the method, backdoor attacks can still be effectively resisted under the scene that the client data sets are non-independent and identically distributed.

The invention belongs to the technical field of federated learning dual optimization, and discloses a federated learning dual optimization method and system based on cluster knowledge distillation and adaptive local differential privacy, and the method comprises the steps: carrying out the data statistical feature extraction of a client; clustering the clients; local training of the client is carried out; applying an adaptive local differential privacy mechanism (ALDP); carrying out model aggregation FedAvg in the cluster; distilling structural knowledge; updating the global model; and evaluating model performance and privacy protection capability. The invention provides a federated learning optimization method which combines client clustering, structural knowledge distillation and a self-adaptive local differential privacy mechanism aiming at the problems of client data heterogeneity, unstable model distillation and difficulty in considering privacy protection and performance in the existing federated learning method. According to the method, client data statistical feature clustering and structural relationship knowledge distillation are combined, and a disturbance mechanism with a dynamic adjustment capability is introduced, so that efficient protection of privacy information is realized while the model performance is improved.

The invention discloses a federated learning stability improvement method and system based on a block chain and knowledge distillation, and relates to the technical field of federated learning. The federal learning stability improvement method based on the block chain and the knowledge distillation comprises the following steps of performing double-model training; obtaining a fault risk assessment value; and verifying the smart contract. According to the invention, dual-model training is carried out through the obtained local private financial data of each participant client, the dual-model training result is uploaded to the constructed block chain data sharing platform, the fault risk assessment value is obtained and whether a single-point fault risk instruction is sent is determined, if yes, smart contract verification is carried out, and if not, the smart contract verification is carried out. And otherwise, completing federated learning optimization, so that the effect of improving the matching degree of the financial customer-oriented unlabeled public data and the client data volume in the federated learning scene is achieved, and the problem of low matching degree of the financial customer-oriented unlabeled public data and the client data volume in the federated learning scene in the prior art is solved.

The invention discloses a non-independent identically distributed data asynchronous federal learning method based on an improved aggregation algorithm. The method comprises the steps that a server initializes a global model and issues the global model to all clients; and the client performs local training on the received global model by using local data, and uploads the model and model parameters to the server after training is completed. Then, the server adjusts a model lag degree based on a client data volume proportion, calculates model difference consistency, client historical contribution stability, old degree penalty of the client model and cosine similarity of the client model and the global model based on parameters of the client model and the current global model, and generates an asynchronous federal aggregation factor accordingly; and updating the global model parameters to generate a new global model. And finally, testing the global model by the server, and judging whether the learning process is stopped or not. According to the method, fair and effective model aggregation can be realized, and the model convergence stability and the final model detection precision are improved.

Implementations described herein are directed to a framework for decentralized learning of large global machine learning (ML) model(s). In various implementations, remote processor(s) of a remote system can identify a global ML model, select client devices to participate in a given round of decentralized learning of the global ML model, and transmit, to each of the client devices, a processed version of the global ML model that is of a reduced transferrable size. Further, client device processor(s) of a client device can receive the processed version of the global ML model, obtain corresponding client data, perform partial model training, based on processing the corresponding client data, for the processed version of the global ML model to generate a corresponding update, and transmit the corresponding update back to the remote system. Moreover, the remote processor(s) can update, based on at least the corresponding update, the global ML model.

The invention relates to a privacy protection heterogeneous federal learning method based on split learning. According to the method, federated learning and split learning are combined, privacy protection on client data in distributed collaborative learning is enhanced by adopting a proper model decomposition strategy, and negative effects of data isomerism are reduced by combining a private group normalization layer into a convolutional neural network. The method comprises the following stages: an initialization stage, a model broadcasting stage, a model training updating stage, a model aggregation stage and a training stop judging stage. Compared with the prior art, the method has the advantages that high model prediction accuracy can be kept under the scene of high client data isomerism in the Internet of Things, the data leakage risk is reduced, and the calculation and communication cost of the client is reduced.

Transport networks, network elements, and methods of use are described herein, including a transmitter comprising a client-side input, transmitter circuitry, and antennas. The client-side input is configured to receive baseband signals having client data encoded therein. The transmitter circuitry is configured to receive the baseband signals from the client-side input and generate antenna feed signals based on the baseband signals. The antennas are configured to receive the antenna feed signals from the transmitter circuitry, generate radiated signals based on the antenna feed signals, and couple the radiated signals into a hollow waveguide. Each of the radiated signals is a radiated electromagnetic wave configured for coherent detection and has a frequency in a range between 300 Gigahertz (GHz) and 10 Terahertz (THz).

The invention belongs to the field of artificial intelligence, and relates to an information recommendation method, which comprises the following steps: acquiring customer data and customer behavior feature information of a plurality of customer terminals; constructing client feature vectors of the plurality of client terminals based on the data and the information; a deep clustering model is adopted to perform clustering analysis on the client feature vectors, and a client group to which each client terminal belongs is determined; determining a target sub-customer group from the customer groups based on a marketing strategy, and predicting the probability of adding the instant messaging group to the customer terminal of the target sub-customer group by adopting a prediction model; and adding the target client terminals of which the probability values are greater than a preset probability threshold value in the target sub-client group to the instant messaging group so as to perform information recommendation on the target client terminals in the instant messaging group. The invention further provides an information recommendation device, equipment and a medium. In addition, the invention also relates to a block chain technology, and client data and client behavior feature information can be stored in a block chain. The accuracy of information recommendation can be improved.

According to the matrix multiplication operation method and device and the storage medium, an original input matrix of a client side is packaged and encrypted through homomorphic encryption, a server side is allowed to operate in a ciphertext mode, and therefore the data privacy of the client side is protected. Afterwards, verification is carried out on the result of matrix multiplication of the server side by using homomorphism of encryption operation through homomorphic hash, and the matrix multiplication and the result of matrix multiplication form a closed loop through a polynomial packaging technology, so that the data security of the client side and the verifiability of a reasoning result are effectively ensured. Moreover, under dual verification of commitment verification and result verification, the accuracy of the target operation result is ensured.

The invention belongs to the technical field of federated learning, and particularly relates to an autonomous adjustment and data balance federated learning system and method based on an incentive mechanism. The system comprises a client screening module for global heterogeneity perception, which is used for realizing dynamic evaluation and screening control of client data quality through a global label distribution offset perception mechanism; the server-side utility evaluation and reverse auction module is used for quantifying the actual contribution value of the client to global model training and realizing the distribution of excitation resources through an auction mechanism; and the client participation rate self-adaptive adjustment module is used for guiding the client to autonomously optimize the participation frequency according to the historical income and cost of the client based on a utility function so as to realize a long-term participation behavior. The method has the characteristics that an incentive mechanism is taken as a core, high-value participants are dynamically identified in combination with data characteristics and participation behaviors of a modeling client, and the system stability and the model performance are improved through a verifiable return strategy.

The invention provides an agro-ecological member data security management method and system, which is applied to the technical field of data security management, and is characterized in that client data abstracts, server data abstract comparison, chain structure storage, multiple independent subject confirmation vouchers and multiple security verification mechanisms are introduced; the method effectively solves the problem of integrity and non-tampering verification of the agro-ecological member data in a multi-subject, long-period and dynamic updating environment, ensures the truth and credibility of the data, and can effectively solve the problem of integrity and non-tampering verification of the agro-ecological member data in the multi-subject, long-period and dynamic updating environment. And the truth and credibility of the data are ensured.

Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support cooperative training of machine learning (ML) models that preserves privacy in untrusted environments. For example, a server (or cloud-based computing device(s)) may be configured to “split” an initial ML model into various partial ML models, some of which are provided to client devices for training based on client-specific data. Output data generated during the training at the client devices may be provided to the server for use in training corresponding server-side partial ML models. After training of the partial ML models is complete, the server may aggregate the trained partial ML models to construct an aggregate ML model for deployment to the client devices. Because the client data is not shared with other entities, privacy is maintained, and the splitting of the ML models enables offloading of computing resource-intensive training from client devices to the server.

The invention discloses a federated learning privacy protection method based on client grouping, and relates to the technical field of data security, the method provides privacy protection of different levels for different clients in an environment in which client data is not independently and identically distributed, and a central server groups the clients with similar data distribution based on Jensen-Shannon divergence; quantizing the balance degree of a client local data set through a balance data set, determining privacy protection levels among different groups, adding interference noise of a specific level to model updating of clients in the groups, and then performing intra-group aggregation; according to the method, for different privacy protection requirements between the clients, the clients are grouped, interference noise of different sizes is added to different groups, the requirement for privacy heterogeneity is met, meanwhile, the noise interference degree in the whole system is reduced, the global model precision is improved, and the method has the advantages of being high in practicability and easy to popularize. The problem of low global model precision caused by traditional differential privacy is solved.

Provided in the present application are a large-model training method, apparatus and device, and a storage medium. A large model comprises an adaptor header, a model main body and an adaptor tail, wherein the adaptor header comprises a first cloud part and a first local part, and the adaptor tail comprises a second cloud part and a second local part; and during the process of training the large model, the first cloud part, the model main body and the second cloud part are deployed on the cloud, and the first local part and the second local part are deployed on a local client. In the method, only the adaptor header and the adaptor tail are distributed to the cloud and the client, encrypted client data is used to perform model training for an adaptor part, the majority of the structure of the model remains on the cloud, and cloud computing power is used to perform training and inference, thereby ensuring the computing power performance; in addition, an adaptor fine-tuning mechanism is effectively used, thereby realizing effective training of the large model while securely and fully utilizing the computing power.

The invention relates to the technical field of medical image segmentation, and discloses a federated medical image segmentation method based on client consistency and adaptive disturbance, and the method comprises the following steps: constructing a supervised client image segmentation network, an unsupervised client image segmentation network, and a server global network; an interaction mechanism between clients is constructed, difference loss between the clients is obtained, the problem of model degradation during global model aggregation is reduced by reducing model distances between different types of clients, and a high-quality aggregated global model is completed; the noise intensity in the learning process of the unsupervised client is dynamically adjusted by using the local model performance of the unsupervised client so as to adapt to the learning difficulty of different client data sets, and the learning quality of the unsupervised client is enhanced so as to enhance the performance of the global model. The method is beneficial to more accurate medical image segmentation in a federal semi-supervised scene, and the segmentation capability of a global model is improved.

A method for providing access to objects associated with a particular client in a cloud storage system is disclosed. The method includes the steps of establishing a connection with a user, providing a client namespace associated with the client to the user, where the client namespace represents objects stored on the cloud storage system and objects stored on a private storage system apart from the cloud storage system, receiving a request from the user to access an object stored on the private storage system, and providing information to the user to facilitate access to the object stored on the private storage system by said user. Other systems and methods are also disclosed. Important advantages of the present invention are facilitated by separating the logic for user access (control plane) from the actual storage (Storage plane). Private file system access can still be managed from the cloud, while keeping the client data private.

A system, method, device, and data platform for managing a tax return. A user profile is received. Client data is automatically aggregated according to the user profile. The client data is tokenized in one or more tokens associated with the client. One or more relevant tax return strategies are determined based on the client data and the one or more tokens. A template tax return is automatically created based on the one or more relevant tax return strategies. The template tax return is populated with the client data to generate the tax return for the client.

The invention belongs to the technical field of computers, and particularly relates to a forgetting-free and data-free distillation method for heterogeneous federal learning. The method comprises the following steps: 1) initializing a generator at a server side, training the generator by jointly optimizing fidelity loss, diversity loss and transferability loss, ensuring that generated samples are aligned with client data distribution and are diversified, and introducing an elastic weight consolidation (EWC) penalty term at the same time; 2) in each round of communication, parameters of a historical difference generator are updated by accumulating differences of the historical generators, so that samples containing historical round knowledge can be synthesized; 3) performing knowledge distillation by using the samples synthesized by the current round generator and the historical difference generator, and minimizing the output difference of the global model and the local model in the aspect of sample generation; and 4) after knowledge distillation is completed, performing heterogeneous fine adjustment on the global model by introducing the difference between the historical global model and the local model.

The invention discloses a federal positioning method based on adaptive aggregation and feature alignment, and belongs to the field of indoor positioning. When federated learning is applied to indoor positioning of the Internet of Things, the problem that data of all terminal devices are not independently distributed is solved, and the influence of the non-independent distributed data on the performance of a global model is reduced by utilizing a self-adaptive federated aggregation coefficient and a feature alignment constraint; according to the algorithm, the aggregation weight of each local model is quantified by comparing the performance difference of a global model before and after local training, and particularly, a more accurate aggregation coefficient is provided for federal aggregation through a variable coefficient of a loss value; considering that differences in client data distribution may cause local model representations to have respective preferences, feature alignment constraints are used to avoid overfitting of local models to improve performance of a global model.

The invention is applied to the technical field of federated learning and privacy protection, and discloses a differential privacy federated learning method for minimizing noise mechanism and sharpness perception, which comprises the following steps: starting federated learning, initializing global model parameters by a server, enabling a global model to comprise a linear conversion layer of the model as an initial layer, then, the server randomly selects part of clients with a fixed number and sends initial model parameters to the clients, and the clients train a local model on the basis of the initial model parameters. According to the differential privacy federated learning method for minimizing the noise mechanism and sharpness perception, the adaptive noise mechanism is introduced, adaptive adjustment of the cutting threshold is combined, it is ensured that the requirement for differential privacy is effectively met in the federated learning process, strong noise is introduced through the large cutting threshold, user data privacy is ensured, and user experience is improved. And along with the deep training, the cutting threshold value is gradually reduced, the noise is gradually reduced, and the balance between privacy protection and model precision is ensured.

The invention provides a fair federated learning method based on function encryption, designs a privacy protection federated learning framework-QPFFL based on multi-client function encryption MCFE and a privacy protection reputation mechanism PPRM, and aims to solve challenges such as quantum resistance, privacy protection and fairness in federated learning. Wherein the privacy of client data is ensured through multi-client function encryption, and safe aggregation of the model is realized. A privacy preserving reputation mechanism identifies and mitigates malicious behavior, evaluates the contribution of each client, and adjusts model weights according to their reputation values. Through the integration of the MCFE and the PPRM, the framework can resist attacks such as convenient vehicle building and poisoning, and the fair model distribution is realized while the data privacy is not damaged.

The invention discloses an audio modal data heterogeneous federal learning method and application, and the method comprises the steps: selecting a plurality of clients, sending global model parameters and global knowledge to the selected clients, and enabling the selected clients to execute local model training; receiving and aggregating local model parameters and local knowledge uploaded by the selected client; updating global parameters and global knowledge based on the aggregated local model parameters and local knowledge in combination with historical global knowledge; and reselecting a plurality of clients, and sending the updated global parameters and global knowledge to the plurality of reselected clients for next round of training until the global model meets a set condition. Compared with the prior art, the audio modal data heterogeneous federated learning method provided by the invention has the advantages that the optimized model parameters have higher precision, the precision of the global model is further improved by the better local model, and the problem of stability reduction caused by client data heterogeneous is solved.

The invention relates to a supernetwork-based balanced federal learning personalization and generalization method and system, and the method comprises the steps: carrying out the flexible switching of a personalization stage and a generalization stage, generating a generalization model through the aggregation of embedded vectors, and improving the overall generalization performance of the model; meanwhile, a personalized model is generated by using a client embedded vector, and the expression of the model under specific data distribution is enhanced. In the reasoning stage, a Shannon entropy-based dynamic selection mechanism is introduced, so that the accuracy and reliability of model reasoning under different data distributions are ensured, and the user experience is improved. In order to solve the problem of cross-client data isomerism in an edge intelligent scene, a hierarchical federated learning framework for generating a personalized model and a generalization model based on a super network is provided, and the framework can effectively balance personalized requirements and generalization requirements of an intelligent terminal.