196 results about "Local machine" patented technology

Techniques of updating machine learning models in a network include combining global and local models at each electronic entity of a network. For example, a first electronic entity (e.g., a user device) may train a local machine learning (ML) model based on data collected by the first electronic entity or other entities (e.g., other user devices, servers) and make predictions based on that model. Nevertheless, other electronic entities may also train or store their own local ML models. Accordingly, for more insight about the network and better predictability of the ML models, the first electronic entity may obtain a ML model from a second electronic entity, i.e., a global ML model. Upon receipt of the global ML model, the first electronic entity may aggregate the local ML model and the global ML model to produce an updated global ML model.

The systems and methods for improving pose estimation models are disclosed herein. Digital image data of an environment can be obtained and provided to a first machine learning model. A first confidence metric can be computed for the image. The first confidence metric can be compared with a threshold value and provided to a second machine learning model. A second confidence metric can be generated for training of machine learning models for pose estimation. A generic machine learning model can be updated using model parameters from trained local machine learning models.

An example method of displaying windows in a remote desktop system, includes: obtaining, by a remote desktop client executing on a local computer having a local operating system (OS), information relating a first window to a first virtual desktop generated by the local OS; sending the information from the remote desktop client to a remote desktop server; setting, by the remote desktop server, a tag in a remote window object representing a remote window that corresponds to the first window based on the information, the remote window generated by a remote OS on a remote computer; receiving, at the remote desktop client, at least a portion of the remote window object including the tag; and displaying, by the remote desktop client in cooperation with the local OS based on the tag, the first window on the first virtual desktop.

Systems and methods for implementing a balanced client selection for decentralized machine learning (BCS-DL) that can be utilized in both decentralized machine learning and hybrid machine learning for vehicle environments are described. For example, a vehicle can include a processor device training a machine learning model using local data, and a controller device performing balanced client selection in real-time to communicate the local machine learning model with a connected vehicle for decentralized machine learning. Hybrid machine learning combines aspects from federated learning and decentralized machine learning approaches. The disclosed BCS-DL system is designed to execute balanced client selection in real-time for vehicles that are acting as clients in a hybrid machine learning infrastructure. The balanced client selection also calculates a training contribution estimation (TCE) and a model weight computation (MWC) to mitigate imbalance in the data distribution incurred by non-Independent, Identically Distributed (non-IID data) related to decentralized and hybrid machine learning.

The invention discloses a multi-robot path coordination system based on reinforcement learning, and the system comprises a state collection module which is used for collecting the operation state and environment perception information of each robot, and generating an original data set; the state modeling module is used for constructing local state representation for each robot in each decision period; the opponent perception module is used for executing opponent learning perception training and outputting opponent parameter estimation vectors; the improved LOLA module is used for generating a final estimation result on the basis of self-strategy updating of the traditional LOLA; the strategy updating module is used for correcting the updating direction of the reinforcement learning strategy of the local machine and generating a feasible action set; the arbitration decision module is used for executing arbitration solution in combination with the candidate path action distribution to generate a scheduling result; and the execution feedback module is used for issuing the control action to a robot executor for execution, generating a path coordination result and storing the path coordination result. According to the invention, path coordination of multiple robots is realized.

The invention provides an unmanned aerial vehicle group obstacle avoidance method and system based on least square and distributed arbitration, and belongs to the field of multi-unmanned aerial vehicle path planning, and the method comprises the steps: maintaining the historical observation data of a dynamic obstacle; a trajectory prediction model is constructed based on historical observation data, and a future trajectory prediction set is generated through least square fitting; selecting an optimal prediction model from the trajectory prediction set through a backtracking verification strategy; on the basis of a preset distributed arbitration rule, whether the local machine obtains the release right of the obstacle prediction information or not is judged; packaging the trajectory information corresponding to the optimal prediction model into a standard message by using the unmanned aerial vehicle which obtains the release right, and broadcasting the standard message to the cluster; and incorporating the obtained predicted trajectory message into collision avoidance constraint of a planner, performing local trajectory planning, and generating a collision-free flight path. According to the method, the safety, the collaboration and the passing efficiency of the cluster system in a real complex scene are remarkably improved.

Embodiments are disclosed for a group security scheme for an autonomous vehicle engaged in a collaborative machine learning approach. As an example, a method comprises: generating, in a vehicle, a digital signature based on a first key and a second key, both of the first key and the second key received from a group manager, and transmitting a message signed with the digital signature to a collaborator, the message including coefficients of a local machine learning model of the vehicle. In this way, an accuracy of the local machine learning model of the vehicle may be increased, while a privacy of the vehicle is increased.

The invention discloses a multi-cluster hot debugging method based on Service Mesh, and belongs to the technical field of cloud native software development. Through a debugging instruction, a multi-cluster intelligent routing gateway establishes a unified and logically isolated debugging session and a virtual network environment for a developer terminal and a plurality of target Kubernetes clusters; the gateway dynamically allocates a local port for a debugging task from a global resource pool, and instructs a target cluster to create a Pod; the gateway serves as a center node, performs cross-cluster routing conflict detection, generates a globally consistent dynamic routing rule and issues the dynamic routing rule to a target cluster; and finally, forwarding the online traffic matched with the rule to an appointed port of a developer local machine through a proxy Pod and a gateway. Through a centralized intelligent coordination architecture, various conflicts in a multi-cluster environment are fundamentally avoided, and the debugging efficiency, stability and automation level are remarkably improved.

Systems and methods for federated model validation and data verification are disclosed. A method may include: (1) receiving, by a local computer program executed by client system, a federated machine learning model from a federated model server; (2) testing, by the local computer program and using a policy service, the federated machine learning model for vulnerabilities to attacks; (3) accepting, by the local computer program, the federated machine learning model in response to the federated machine learning model passing the testing; (4) training, by the local computer program, the federated machine learning model using input data comprising local data and outputting training parameters; (5) identifying, by the local computer program using the policy service, accidental leakage and / or contamination by comparing the training parameters to the input data; and (6) providing, by the local computer program, the training parameters to the federated model server.

This disclosure provides a method for machine learning model training in a communications network. The method comprises transmitting from a first network node to a second network node a request for a first indication of a label, a second indication of intermediate training results, and / or a third indication of a loss calculation; and receiving at the first network node from the second network node a first indication of the label, a second indication of the intermediate training results, and / or a third indication of the calculated loss; determining at the first network node a model convergence based on the received first indication, the second indication, and / or the third indication, and initiating at the first network node an update of a local Machine Learning (ML) model using the at least one intermediate training result and / or the third indication of the calculated loss; particularly wherein the initiating comprises storing at the first network node the ML model.

A computing device, that is configured to configure a global machine learning model, performs respective electronic risk audits of client devices configured to train respective local machine learning models that correspond to a global machine learning model. Based on respective electronic risk scores of one or more of the client devices, determined via the respective electronic risk audits, the computing device implements one or more parameter privacy adjustment methods on respective parameters received from the client devices prior to using the respective parameters to configure the global machine learning model, wherein respective client devices determined to have higher electronic risk scores have more of the parameter privacy adjustment methods applied than other respective client devices determined to have lower electronic risk scores. The computing device provides, to the client devices, the global machine learning model configured according to the respective parameters as adjusted.

This invention discloses a data processing method, system, device, and medium based on a user-selected local model. The method includes: acquiring a local machine learning model file in response to a user operation; dynamically parsing model metadata to determine input and output specifications; acquiring multimodal target data and preprocessing and adapting it according to the input specifications; calling local hardware resources to execute model inference; parsing the inference results according to the output specifications and outputting them to the target business system. This invention overcomes the closed nature of traditional software-built-in AI models, achieving full utilization of local AI computing power and effective privacy protection of sensitive data. Through a highly abstract underlying adaptation architecture, this invention is not dependent on specific application scenarios and can be widely integrated as a general-purpose underlying technology into various industry software such as video restoration, medical image analysis, professional image retouching, and industrial inspection, giving the software flexible AI capability scalability.

The invention discloses a crystal oscillator-free Bluetooth chip pairing method and device and a communication system, and the method comprises the steps: switching a preset frequency offset value for polling between a paging scanning state and a query scanning state, so as to search an ID packet sent by host equipment in the query state; when the ID packet is received, a slave equipment information packet of the host is sent to the host equipment; receiving a host equipment information packet sent by the host equipment in response to the slave equipment information packet, so that the host equipment can establish Bluetooth connection with a local machine; and sending a connection request to the host device according to the address information of the host device loaded in the host device information packet in the paging state to establish a Bluetooth connection with the host device so as to realize Bluetooth pairing between the local machine and the host device. For low-power-consumption equipment, the Bluetooth chip without the crystal oscillator is ensured to be paired with other equipment to establish connection. The power consumption of low-power-consumption equipment is reduced, the development difficulty, development cost and period of products are reduced, and the equipment is more miniaturized.

System that features the following: a processing unit that is functionally connected to a memory; an AI (Artificial Intelligence) platform that exchanges data with the processing unit, wherein the AI ​​platform is designed to train a machine learning (ML) model, wherein the AI ​​platform has the following features: a registration manager to register participating entities in a cooperative relationship, to arrange the registered entities in a topology, and to establish a topological data transmission protocol; an encryption manager to generate a public additive homomorphic encryption key, AHE key, and distribute it to each registered entity; an entity manager to control direct local encryption of machine learning model weights of local entities on registered entities using a distributed public AHE key, to selectively aggregate the encrypted local machine learning model weights including distributing the aggregated encrypted weights to one or more other participating entities in the topology, or to support local aggregation on the registered entities according to the topological data transmission protocol; where the encryption manager decrypts an aggregated sum of the encrypted local machine learning model weights with a corresponding private AHE key, and distributes the decrypted aggregated sum to each entity in the topology.

Methods and systems for automating surgical procedures in model organisms. The system includes a user input panel, a trained computer vision system, a server, and articulated robotic arm. The system may further include an analytical scale, an anesthesia chamber, a tube labeling system, a tissue freezing system, and a biohazard waste disposal system. The computer vision system communicates with the robotic arm to coordinate tissue collections and common research procedures such as injections via recognition models related to detection, identification, localization, and classification. The central server provides rapid synchronization of data between the local machine and a cloud account system for real-time data analytics. The anesthesia chamber provides standardized administration of anesthesia, the sample labeling system provides 2D barcoded labels according to user input, and the tissue freezing and waste disposal systems enable storage of samples and removal of carcasses following tissue collection, fully automating the necropsy.

This invention discloses a federated averaging algorithm based on residual networks, characterized by high accuracy and efficient communication: Step 1: The server partitions the MNIST dataset into IID and Non-IID categories and distributes them to various clients; Step 2: Each client locally constructs a residual network model; Step 3: The number of communication rounds R between the server and clients is set. In each round, the server randomly selects K clients to participate in the communication and sends the parameters of the global model; Step 4: The selected clients download the parameters of the global model and perform local machine learning training to obtain the trained model parameters; Step 5: The K clients upload the trained model parameters to the server; Step 6: The server updates the global model parameters according to a weighted average aggregation strategy and repeats steps 3-6 until the final global model parameters are obtained in the Rth round of communication. This invention significantly improves accuracy and communication efficiency compared to existing technologies.

Techniques for training a global model for use at a host of oilfield application sites based on raw data obtained from the application sites without direct exposure of the data to the global model. The techniques include developing and distributing a global model with a predetermined set of parameter weights. The model is then locally employed at each application site by a local computer which maintains the integrity of the acquired data during performance of the oilfield application. The data is used to update the parameter weights based on real-time circumstances. Thus, the parameter weights may be transmitted to the centralized computer for updating of the global model. Further, the updated global model may continue to direct other applications and the process continued in a beneficial feedback loop manner.

This invention discloses a method, apparatus, medium, and product for calculating bonding interactions in molecules. The method includes: allocating bonding tasks using a three-dimensional interconnected computing architecture, and constructing a bonding atom set and a bonding task index table matching the allocated bonding tasks within each computing module. This ensures that identical atoms do not need to be repeatedly stored when the computing module executes all bonding tasks, reducing the storage overhead of atomic information; dynamically acquiring and filling atomic information from the local machine or other computing modules into the corresponding storage locations of the bonding atom set, extracting atomic information according to the index table, calculating bonding components in parallel, and accumulating the bonding components of identical atoms to obtain the resultant bonding force; and updating the resultant bonding force and atomic information through inter-module interaction, and re-storing the atomic information according to the updated spatial arrangement. This reduces the amount of data transmission between modules while ensuring the accuracy of molecular bonding interaction simulation, thereby improving the stability and efficiency of the overall molecular dynamics simulation.

Described herein is a system and a method for collaborative training of a machine learning model for pose estimation. The system includes a remote computing device in communication with a local computing device, wherein the remote computing device is configured to instruct the local computing device to receive a first image; and generate a first pose datum as a function of the first image using a local machine learning model. The remote computing device is configured to receive, from the local computing device, the first pose datum; generate a second pose datum as a function of the first pose datum and the first image using a remote machine learning model; and configure the local computing device to retrain the local machine learning model as a function of the second pose datum.

A biological sample analysis system and a biological sample analysis method. The biological sample analysis system includes a plurality of imaging devices, a plurality of local computing devices, and a cloud computing device. Each imaging device captures a series of images from a biological sample. Actions performed by each local computing device include: receiving the series of images from a corresponding one of the imaging devices; and processing the series of images using a local machine learning model to generate an analysis result. The cloud computing device performs actions including: training a cloud machine learning model based on the series of images and the analysis result from the local computing devices to generate an optimized machine learning model; and transmitting the optimized machine learning model to each local computing device to replace or update the local machine learning model.

The invention discloses a cooperative unloading game method and system for joint reasoning and training and application thereof, and the method comprises the steps: selecting a reasoning task from a reasoning task queue, and enabling the deadline of the reasoning task not to exceed a current time point; judging whether the current system resource meets the demand of the reasoning task, if so, executing the reasoning task on a local machine, and if not, pausing the currently executed training task to release the resource; after the training task is paused, whether current system resources meet the requirements of the reasoning task or not is judged; if yes, executing the reasoning task on a local machine; and if not, unloading the reasoning task. According to the method, reasoning tasks and training tasks are jointly scheduled, task unloading among multiple edge servers is coordinated, efficient utilization of resources is ensured, meanwhile, the average response time delay of the reasoning tasks is minimized, and the training process is maintained.

This invention relates to a distributed, covert data reporting method based on blockchain, belonging to the field of data security technology. To ensure the security of the data reporting process, this invention proposes a session key update method based on blockchain and Diffie-Hellman key exchange. Using symmetric cryptography, hash functions, and digital signature technology, it proposes a special data packet construction method. Each participating party periodically scans the blockchain for changes and pulls the new block to its local machine. This invention fundamentally eliminates the need for a handshake between the sender and receiver to establish a secure network communication channel, reducing the adverse effects of attacks such as DDoS attacks; it ensures the integrity and consistency of data packets; it provides data non-repudiation, tamper-proof, and anti-forgery characteristics; and it achieves low-maintenance key distribution and storage.

Proposed are a test and measurement system for evaluating learning models of one or more test devices, a test and measurement procedure, and a computer program. A test and measurement system (100) for evaluating learning models of one or more test devices (102; 104; 106; 108), comprising a test and measurement device (10), includes one or more interfaces (12) configured for data communication with the one or more test devices (102; 104; 106; 108). The test and measurement device (10) further comprises one or more computing units (14) configured to generate stimulus data for the one or more test devices (102; 104; 106; 108).The one or more computing units (14) are configured to provide the stimulus data to the one or more test devices (102; 104; 106; 108) via the one or more interfaces (12), to train one or more local machine learning models by the one or more test devices (102; 104; 106; 108) based on the stimulus data, and to receive training model data from the one or more trained machine learning models via the one or more interfaces (12) from the one or more test devices (102; 104; 106; 108). The one or more computing units (14) are configured to evaluate the quality of the one or more trained machine learning models based on the training model data.

The invention discloses a distributed data storage method and device based on multiple machine rooms, relates to the technical field of data processing, and mainly aims to realize multi-machine-room copy storage of data, so that the stored data can be merged and read from the multiple machine rooms, and the continuity and availability of business application services are improved. According to the main technical scheme, when an agency service of a target machine room receives configuration information of an application service, a to-be-cached machine room is determined from a machine room list corresponding to the agency service, the machine room list is constructed based on the fault rate of each machine room, and the to-be-cached machine room comprises a local machine room and at least one remote machine room; and writing the configuration information into a cache container in the to-be-cached machine room in a specified data structure. The method and the device are used for data storage of business application services.

This application provides a method and apparatus for autonomous auctioning new search tasks in a drone swarm. The method includes: step 1, numbering each member of the drone swarm within the range of 1 to M; step 2, loading the initial task area location database {D}. j},j=1,…,N;Step 3, Load the takeoff point longitude TOLon i Step 4: Load the bidding time threshold THSubmit and the decision time threshold THDecide; Step 5: If a new task region D is received... N+1 If the information is available, then the bid price (Price) will be calculated. i And send it to other drone members in the drone swarm; Step 6, if the time to enter Step 5 is greater than the bidding time threshold THSubmit, then based on the bids of the local drone and other drone members in the swarm {Price}... j}, j=1,…,M make preliminary decisions, and the preliminary decision results are Result i Send to other drone members in the group; Step 7, for each member i (i = 1, ..., M) in the drone group, if the time to enter step 6 is greater than the decision time threshold THDecide, then based on the preliminary decision results received from the local machine and other drone members in the group {Result j The final decision is made by considering the groups},j=1,…,M.

A privacy-preserving authentication method for anti-counterfeiting QR codes using Vision Transformer- (ViT-) based Federated Learning (FL) is provided. In the method, an individual client authenticates an anti-counterfeiting QR code as captured in an image presented to the individual client. A local machine-learning (ML) model of the individual client determines authenticity of the anti-counterfeiting QR code as captured in an image presented to the individual client. The local ML model is initialized as a pretrained ViT-based model, pretrained on the large-scale ImageNet dataset for processing an input image to determine authenticity of the anti-counterfeiting QR code as captured in the input image. The plurality of clients performs a cyclic weight transfer FL process to update respective local ML models of the plurality of clients according to instant pluralities of training data respectively owned by different clients in the plurality of clients while preserving training-data privacy among the different clients.

A method of wireless communication by a user equipment (UE), comprises inputting sensor data, captured at the UE, to a local machine learning model. The method also includes extracting features from the sensor data, with the local machine learning model, while anonymizing the sensor data. The method further includes transmitting the features to a base station, and receiving a handover decision from the base station based on the features. A method of wireless communication by abase station inputs anonymized sensor feature data, received from a user equipment (UE), into a network machine learning model. The base station also inputs handover decision information into the network machine learning model. The method further includes inferring a handover decision based on the handover decision information and the anonymized sensor feature data, and transmitting the handover decision to the UE.

The invention provides a centralized storage method and device for an airborne multi-relation table database. A transaction management unit obtains a transaction operation instruction and sends the transaction operation instruction to a data mirror image table processing unit; the data mirror image table processing unit locally searches whether a corresponding data relation list exists or not based on the transaction operation instruction, if yes, a data table corresponding to the data relation list is locally obtained, and operation indicated by the transaction operation instruction is carried out on the obtained data table; after executing all transaction operation instructions, the data mirror image table processing unit temporarily stores the updated data relation list and the updated data table locally; when a preset condition is met, the updated data relation list and the updated data table are sent to a multi-relation-table storage unit to be stored, and the original data relation list and the original data table of the multi-relation-table storage unit are covered; the system performance and the resource utilization rate can be improved; the method is used for managing data tables in a disk in an airborne embedded database.

The application discloses a data declassification sharing method and device, equipment and medium. The method comprises the following steps: in response to a data sharing request, a local machine acquires original data to be shared; resampling processing or homeomorphism transformation is performed on a data manifold corresponding to the original data to form declassified result data matched with the original data; and the declassified result data is taken as a response result of the data sharing request. Through the technical scheme of the application, data sharing can be efficiently completed while ensuring the safety of shared data, and the efficiency of data sharing is improved.

The application relates to a collision risk quantitative evaluation method applied to low-altitude airspace, and belongs to the technical field of low-altitude risk evaluation. The method solves the problem that an effective method for evaluating collision risks of multiple types of aircraft is lacked in the prior art. The method comprises the following steps: S0, obtaining data information of low-altitude airspace, judging whether an intruding aircraft is a cooperative unmanned aerial vehicle, executing step S1 if the intruding aircraft is the cooperative unmanned aerial vehicle, executing step S2 if the intruding aircraft is a non-cooperative unmanned aerial vehicle, and executing step S3 if the intruding aircraft is a manned aircraft; S1, determining conflict resolution measures involved, generating and outputting a collision probability of the local machine and the cooperative unmanned aerial vehicle, and executing step S4; S2, determining conflict resolution measures involved, generating and outputting a collision probability of the local machine and the non-cooperative unmanned aerial vehicle, and executing step S4; S3, determining conflict resolution measures involved, generating and outputting a collision probability of the local machine and the manned aircraft, and executing step S4; and S4, evaluating a safety situation of the local machine and the intruding aircraft according to the received collision probability.