209 results about "Co-training" patented technology

The invention provides a private weight self-adaptive heterogeneous data federated cooperative training method and system in the technical field of federated learning and privacy computing, and the method comprises the steps: S1, enabling each client to carry out the differential privacy operation on a local data set based on a private weight, and obtaining a desensitized data set, encoding the desensitized data set through a heterogeneous data encoding model; s2, performing semantic alignment on each coding vector through a contrast learning model to obtain an aligned vector set; s3, training a local model through the alignment vector set, generating a local gradient, extracting local model parameters, and uploading the privacy weight, the local gradient and local difference parameters to a server; and S4, the server trains the global model based on the local difference parameter and the global gradient, extracts the global model parameter and issues the global model parameter to each client for training. The method has the advantages that the compatibility, the flexibility and the efficiency of heterogeneous data federation cooperative training are greatly improved.

The invention belongs to the technical field of robot learning, and particularly discloses a robot continuous imitation learning method and system based on a double diffusion model. Comprising the following steps: generating old task data in a submerged space by utilizing a pre-trained latent diffusion model, generating a model through guidance of a task identifier in combination with conditional diffusion, generating an image and state data related to an old task, modeling to construct a diffusion model, realizing multi-modal mapping from a state to an action, and generating a diversified action sequence; high-quality images and state data are generated through a pre-trained variational auto-encoder and a conditional diffusion model, and the consistency of the generated data in time and the accuracy of global semantics are ensured through time sequence continuity constraint and semantic consistency constraint; and performing feedback optimization training on the latent diffusion model. According to the method, the loss weight is dynamically adjusted, and the model is ensured to show good generalization ability and stability in a complex task in combination with collaborative training of an imitation learning strategy and a generator.

The invention discloses a privacy protection data sharing and cooperative training method and system based on federated learning. The method comprises the steps of receiving software development log data, adaptively judging the sensitivity degree according to a data type, dynamically adjusting noise disturbance intensity according to the sensitivity degree to perform data desensitization, and generating a sensitivity index; selecting a feature extraction strategy, extracting time sequence correlation features from the desensitization data, constructing a dynamic graph structure with a weight, and obtaining a time sequence feature vector through iterative fusion; calculating the time sequence correlation of the time sequence feature vector to obtain a data quality score, and setting a contribution weight based on the quality score to perform parameter aggregation; combining sensitivity indexes with data quality scores to construct a security sharing domain, decoupling global training parameters into knowledge fragments in the domain, formulating a recombination rule, and selectively acquiring the required knowledge fragments by all parties for local training. According to the method, deep collaboration is realized on the premise of protecting data privacy, and the collaboration training effect is improved.

The invention discloses a multi-mode driven cross-industry digital twinning universal platform architecture and an implementation method, and relates to the technical field of digital twinning and artificial intelligence. The method comprises the following steps: establishing a multi-modal driven cross-industry digital twinning universal platform, deploying a multi-source heterogeneous data acquisition component in a data access layer to access text, image and time series data, and converting unstructured data into a unified feature space by adopting a Transform-GNN cross-modal encoder in a multi-modal fusion layer; in the large model scheduling layer, feature vectors are analyzed through a multi-modal large model center based on an industry knowledge graph, an algorithm is dynamically matched, and an initial decision strategy is generated; the method comprises the following steps of: establishing a parameterized template library, and supporting security cooperative training of a third-party algorithm scheduling engine on a third-party algorithm, and deploying a lightweight digital twin engine in a twin engine layer: establishing the parameterized template library: pre-defining three templates of geographic space, equipment assets and business processes; deploying the twin model to an edge node by adopting a knowledge distillation method; in the interactive application layer, loading the BIM / GIS model in a lightweight manner through a low-code tool, and completing scene construction through a dragging component; and rendering a twin state in real time through a three-dimensional cockpit, analyzing a natural language instruction and performing corresponding operation.

A system for real-time detection and mitigation of morphing malware in high-density edge networks, consisting of: a data acquisition unit configured to receive, normalize, and encode multimodal telemetry data streams originating from at least one of the following domains: network traffic, process behavior, system call sequences, binary instruction traces, and control flow graphs; the data acquisition unit is further configured to compute feature embeddings over sliding time windows and apply privacy-preserving redactions prior to storage; a generative neural processor that is operationally coupled to the data acquisition unit and configured to generate synthetic morphing malware variants by learning probabilistic transformations of previously observed malicious data representations, maintaining semantic functionality while varying structural and behavioral features; a discriminative neural processor trained adversarially with the generative neural processor, wherein the discriminative neural processor is configured to detect morphing malware by evaluating a probability distribution over multimodal telemetry embeddings and classifying anomalous process and flow behaviors in real time; a coordination processor that is communicatively connected to both the generative neural processor and the discriminative neural processor and is configured to orchestrate adversarial co-training, regulate detection thresholds, calculate reinforcement-based penalties for false negative results, and trigger countermeasures as soon as a detection confidence level exceeds a predefined adaptive threshold; a secure, system-integrated inference and enforcement unit configured to perform low-latency countermeasures at the network edge, including selective packet filtering, flow isolation, process interruption, or system microsegmentation, based on instructions from the coordinating processor; and a hardware-embedded security enclave that is embedded in the system and configured to store cryptographic keys, neural model parameters, and integrity affirmation data to ensure the confidentiality, authenticity, and immutability of model artifacts and policy configurations.

The invention relates to the technical field of intelligent dialogues, and discloses a dialogue marketing strategy optimization method based on artificial intelligence, and the method comprises the following steps: collecting the voice, text, facial expression and physiological signals of a user in real time through a multi-modal perception assembly of a terminal device, constructing a dynamic emotion map, and extracting a multi-modal feature vector; a cross-modal information processing module is utilized to align the multi-modal data through a comparative learning algorithm, and causal relationship description of user behaviors and strategies and strategy risk scores are generated; cooperatively training a global strategy model through differential privacy and homomorphic encryption technologies; combining the dynamic emotion map and a causal model to generate an emotion adaptive dialogue script, and optimizing strategy selection through a reinforcement learning algorithm; and dynamically updating the emotion map, the risk score and the global model through a closed-loop feedback mechanism to form a real-time optimized strategy generation system. According to the invention, the practicability of artificial intelligence to real-time services can be improved.

The invention relates to the field of finance, and discloses an AI-based investment project feasibility intelligent analysis and decision support system, which comprises a multi-modal data fusion engine used for extracting a feature vector of an unstructured text through a BERT variant model, carrying out joint coding on the feature vector and a structured data feature, and outputting fused multi-modal data; the dynamic knowledge graph construction module is connected with the multi-modal data fusion engine; the deep reinforcement learning decision module comprises a feasibility prediction network and a risk assessment network which are cooperatively trained; an interpretable AI module; and a closed loop feedback module. By means of a multi-modal data fusion engine and a deep reinforcement learning decision module, automatic processing and intelligent analysis of data are achieved, and the analysis time of a single project is greatly shortened to 8 minutes. The high-efficiency processing speed enables the investment institution to quickly respond to the market change, timely grasp the investment opportunity, and occupy the decision-making precedence in the financial market which changes instantaneously.

The invention discloses a cue word optimization training method and system based on multi-modal large model supervision. The method comprises the following steps: screening and optimizing a plurality of initial cue words by adopting a simple multi-modal large model, a complex multi-modal large model and a first text large model to obtain a simple optimal cue word and a complex optimal cue word; inputting the simple optimal cue word, the training set and the manual calibration problem into a simple multi-modal large model for training to obtain a simple total loss value; judging whether the total simple loss value is within a preset threshold range, and if yes, ending training; and otherwise, repeating the optimization of the process until the simple total loss value is within the preset threshold range, stopping training, and taking the simple optimal cue word obtained by the last update as the target optimal cue word. According to the method, manual intervention is reduced through cooperative training of a complex large model and a simple large model; manual calibration data and the reasoning process of a complex large model are combined, and the cue word accuracy is improved through double supervision.

The invention discloses a cross-modal large model construction method and system based on track spatio-temporal characteristics, and belongs to the crossing field of artificial intelligence and dynamic spatio-temporal data processing, and the method comprises the steps: carrying out the sliding sampling and spatial distribution difference judgment through the semantic dynamic segmentation of multi-scale track spatio-temporal data, and generating spatio-temporal data blocks with consistent semantics; designing a space-time encoder of a hybrid architecture, extracting track time sequence association and spatial features, and unifying dimensions; constructing a text space-time fusion mechanism, and dynamically adapting cross-modal features by means of an anchor interface and gating fusion; a staged instruction fine tuning strategy is adopted, semantic alignment of space-time and text features is optimized firstly, then model top-layer parameters are trained cooperatively, complex scene adaptation is enhanced in combination with instruction difficulty progression and hard sample mining, and space-time constraint regular terms are introduced to guarantee output rationality. According to the method, high-precision cross-modal reasoning capability is provided for scenes such as track analysis and track prediction.

The invention relates to a hyper-dimensional calculation model cooperative training method and device based on federated learning, and belongs to the field of distributed machine learning and data privacy protection. The device comprises a central server and M distributed client devices which communicate through network connection, the method comprises the following steps: S1, initializing parameters; s2, broadcasting parameters; s3, local super-dimensional calculation model parameters are updated; s4, the similarity between the two is calculated, and the local training data volume is counted; s5, uploading to a server; s6, calculating parameters of the global super-dimensional calculation model based on a dynamic weighted aggregation method; s7, judging whether convergence is carried out or the maximum training round number is reached; if yes, training is ended, and global super-dimensional calculation model parameters are output; otherwise, returning to the step S3; and S8, the user executes the classification task and outputs a prediction classification result. According to the method, the training efficiency and the global model performance of federal learning under a super-dimensional calculation model can be improved on the premise of ensuring data privacy and security.

The invention provides a cross-modal feature fusion method based on joint attention, which combines multi-modal data from different sources to predict the relative position of a target to be tracked, and comprises the following steps of: firstly, designing a cross-modal data representation network model for the data of different modals; generating a time sequence high-dimensional implicit code retaining key dependency information from the original data through a code conversion algorithm; secondly, a heterogeneous attention network unit is designed, and features of time sequence high-dimensional implicit codes corresponding to different modal data are extracted through two different forms of attention; and finally, through a cooperative training mechanism, information complementation among different modal data is utilized to generate the most effective feature representation for a time sequence prediction model, and accurate prediction of the moving position of the tracked target is realized.

The invention discloses an efficient federal collaborative learning framework for edge-cloud heterogeneous model co-modeling, and belongs to the technical field of federal learning. In order to solve the problems that traditional federated learning is low in one-way knowledge transmission efficiency, privacy protection and model performance are difficult to balance and only isomorphic model parameter aggregation is supported, mutual training among diversified models is realized by combining a cloud edge interactive handshake protocol (CEHP) and an information gain evaluation mechanism (IGVE); and an attention feature fusion mechanism (AFF) is designed to realize feature exchange between heterogeneous models of different targets, and meanwhile, an inverse gradient equalizer is designed to promote dynamic evolution of the models. By means of the multi-target strategy gradient algorithm, the relation between different targets is deeply mined and balanced, and the efficient federal collaborative learning framework is comprehensively and meticulously optimized. According to the method, generalization of the cloud side model and fine granularity of the edge side model are promoted, so that edge-cloud win-win cooperative training is realized.

The invention provides a big data integration and distribution unit component of a hierarchical architecture and a use method, relates to the technical field of data integration and processing, and solves the problems of real-time integration, unified processing and safe distribution of multi-source heterogeneous data. The device adopts a five-layer modular architecture; a data access layer supports multi-source data access and is buffered through Kafka; the cleaning conversion layer performs data cleaning and standardization by using a FlinkSQL, an ETL engine and a rule engine; the intelligent distribution layer judges a data distribution path in combination with a rule engine and a machine learning model; the storage calculation layer adopts a cold and hot separation storage strategy and supports parallel operation of Spark batch processing and Flink stream processing; and the service and security layer provides an interface through an API gateway, and guarantees data security in combination with AES-256 encryption and RBAC access control. Besides, dynamic resource scheduling is realized through Kubernetes, cross-domain model cooperative training is supported by utilizing a federated learning technology, the intelligent shunting decision-making capability is improved, various industrial protocols such as MQTT and Modbus are compatible, and VXLAN tunnel transmission is supported.

The invention belongs to the field of crossing of remote sensing visual questioning and answering and artificial intelligence, and particularly relates to a remote sensing visual questioning and answering method based on a large language model and a multi-level attention mechanism. Comprising the following steps: 1, constructing a MogaNet-based semantic segmentation network; 2, text coding is carried out through a large language model; 3, constructing a mixed attention guiding module; 4, constructing a bidirectional gating cross attention module; and 5, constructing and cooperatively training a remote sensing visual question-answer network. According to the MLaVQA framework provided by the invention, efficient and accurate visual question-answering processing of remote sensing images can be realized, when large-scale multi-modal remote sensing data is processed, complex semantic questions can be quickly answered, fine monitoring and intelligent management of earth resources are realized, the data utilization efficiency and decision support capability are remarkably improved, and the method is suitable for large-scale multi-modal remote sensing data processing. And technical support is provided for natural resource management, ecological protection and sustainable development.

The invention discloses a collaborative training method, medium and device based on an LLM large and small model, and the method comprises the steps: S1, obtaining a huge knowledge system through the combination of the language understanding capability and pre-training capability of a general large model, and carrying out the cold start of an AI service 0 sample needed by a business scene, and enabling the AI service 0 sample to be online; s2, performing a small amount of annotation on sample data generated by a business scene, performing fine tuning on the general large model to form a scene large model, performing continuous learning to enable the scene large model to have knowledge in the field, and quickly improving an algorithm effect; and S3, distilling knowledge in the field obtained by the scene large model into a plurality of small models, and fusing results of the plurality of small models by using a scoring mechanism to realize collaborative training of the large and small models. According to the method, any scene text service can be subjected to cold start online under the condition of limited hardware resources, large and small model cooperative training of large model knowledge can be obtained through a small amount of labeling, and the model learning ability and the working efficiency are greatly improved.

The invention discloses a robust federated learning method for processing heterogeneous noise and non-independent identically distributed data, and belongs to the technical field of federated learning. The method provides a robust learning framework of two-stage client quality perception. The method comprises the following steps of: 1, constructing a category-level loss vector and clustering by using a Gaussian mixture model, and accurately dividing a clean and noise client set; stage 2, performing differential training: performing standard training on the clean client; dual-network cooperative training, dynamic sample screening and exchange, and a self-distillation and entropy regularization mechanism are introduced to a noise client, so that robust learning is realized; in the global aggregation stage, a distance sensing weighting strategy is further adopted to dynamically suppress the influence of a noise client; according to the method, original data does not need to be shared, the robustness and generalization performance of the federated learning model in the coexistence environment of heterogeneous noise and non-independent identically distributed data can be effectively improved, and the method has wide application value in the fields of medical images, financial risk control and the like.

The invention relates to an access control strategy automatic generation system and method based on multi-task learning, and the method comprises the steps: carrying out the word segmentation, cleaning and embedded vector conversion of an original access control text through a data preprocessing module, and constructing a normative input format; the feature sharing layer module is used for extracting deep semantic features of a text through multi-layer bidirectional coding and an attention mechanism and providing unified representation for downstream tasks; the access control statement identification module is used for judging whether each sentence in the text is an access control statement or not and realizing automatic identification of strategy related contents; and the attribute extraction and annotation module is used for annotating words in the access control statements and extracting subject, object and operation access control attributes. A word coding layer and a sentence coding layer are shared, local and global attention mechanisms are combined, key information of a text is extracted, the semantic understanding ability is enhanced, and cooperative training of statement recognition and attribute extraction is achieved; a conditional random field CRF structure is used for sequence labeling, and the structural rationality of attribute labels is ensured.

The invention relates to the technical field of intelligent coal mine safety production, and discloses an intelligent coal mine safety early warning method and system based on deep learning, and the method comprises the steps: constructing a difficulty evaluation function, and achieving the progressive learning from simple to complex; based on a difficulty assessment result, a model-independent meta-learning algorithm is realized, so that the model quickly adapts to new mining area characteristics; constructing a privacy protection federated learning framework by using the meta-learning model, and realizing multi-mining-area cooperative training; a continuous learning module is constructed, and original experience is reserved when new knowledge is learned; constructing a meta-knowledge evaluation module to realize cross-mining-area safety knowledge sharing; according to the invention, the security risk identification accuracy is improved; multi-mining-area cooperative training is realized on the premise of protecting data privacy; the method has continuous optimization capability and effectively solves the problem of model drift; and efficient sharing and migration of cross-mining-area safety knowledge are realized.

The embodiment of the invention provides an end-cloud cooperative training method, system and device in the field of artificial intelligence, and can be used for stripping an embedding layer to terminal training in an end-cloud cooperative training process so as to improve the privacy security of end-side data. The method comprises the steps that a client uses training data as input of a representation layer to obtain a representation vector, the representation layer is used for obtaining a vector corresponding to the input data from a representation word list stored in the client, and the representation word list is stored in the client; the client sends the representation vector to the cloud platform, so that the cloud platform takes the representation vector as the input of a language model deployed at the cloud platform side to obtain an output feature; the client receives an output feature sent by the cloud platform, wherein the output feature is obtained by inputting a representation vector into a language model by the cloud platform; and the client calculates a loss value by using the output feature, updates the representation layer according to the loss value, and sends the loss value to the cloud platform, and the loss value is used for the cloud platform to update the language model.

The invention relates to a multi-agent reinforcement learning training method, system and device driven by an unreal engine and a medium. The method comprises the following steps: acquiring core attribute data of multiple agents, including real-time communication bandwidth, initial communication topology configuration, training stage progress, single agent computing power and cluster overall computing load state; analyzing the real-time communication bandwidth and the initial communication topology configuration to form an optimized topology configuration scheme; in combination with an optimized topology configuration scheme and a training stage progress, generating a cooperative training progress state by quantifying progress differences among intelligent agents; extracting load balance data according to the single agent computing capability and the cluster load state; integrating load balance data, a collaborative training progress state and an optimized topology configuration scheme, and constructing a multi-layer collaborative training framework; and integrated optimization is carried out to generate a cooperative training scheme for guiding multi-agent communication, training and resource allocation, so that the training efficiency is effectively improved, the resource load is balanced, and the cooperative synchronism is enhanced.

The invention discloses an artificial intelligence network optimization training system and method based on deep learning, and the system comprises a request analysis module which is used for determining a training task type and an initial model hyper-parameter, and obtaining an available edge device list; the parameter monitoring module is used for determining a model index monitoring threshold value, monitoring model indexes in the training process in real time and adjusting corresponding model hyper-parameters; the multi-target optimization module is used for initializing target weights and calculating a multi-target optimal solution set; the self-adaptive training module is used for generating a training strategy and optimizing and adjusting the training strategy; and the cooperative training module is used for allocating training tasks to the available edge devices and feeding back model indexes in the training process to the parameter detection module. According to the invention, the training mode is dynamically adjusted according to the specific parameters of the artificial intelligence network, multi-objective optimization is realized, distributed training of edge devices is supported, and the robustness of the model is improved, so that the training precision and efficiency are improved, and the application range is expanded.

The invention provides a marine image data identification method and system. The method comprises the following steps: step 1, inputting an original marine image; 2, a visual feature extraction module extracts hierarchical features through a multi-scale convolutional neural network; 3, pre-training an LLM semantic reasoning hierarchical attention mechanism semantic mapping multi-task cooperative training framework by the language model agent module; and 4, a dynamic strategy optimization module reinforces learning optimization strategies to adjust identification parameters in real time, and generates a preliminary identification result. And 5, when the user needs to optimize, combining user feedback, a data enhancement interactive learning mechanism and a semantic consistency data enhancement optimization model to realize model updating and parameter adjustment, and returning to the step 2 again. And when the user does not need optimization, outputting a final identification result of the ocean image. According to the invention, a visual coding technology is combined with the semantic understanding capability of LLM, and a recognition solution capable of adaptively processing a multi-modal ocean image is constructed.

The invention discloses an edge caching method for federal deep reinforcement learning based on semantic enhancement, and the method comprises the steps: obtaining request original data and a candidate content set, and carrying out the multi-modal feature extraction; calculating a cache utilization rate, request diversity and a dynamic semantic matching weight; calculating the semantic similarity between the request content and the candidate content, and performing adjustment in combination with a dynamic semantic matching weight to obtain a weighted semantic similarity; generating action probability distribution according to the state of the node, and fusing the action probability distribution with the semantic attention weight to obtain action preference distribution; action execution is selected according to the action preference distribution, and rewards are evaluated; and periodically executing federal cooperative training optimization, locally training a semantic perception strategy network by adopting an SAC algorithm, executing multi-factor dynamic weighted federal aggregation to update a global model, and distributing the global model to each node. According to the method, the edge cache hit rate and the semantic hit rate can be improved, the service response delay is reduced, and the robustness of heterogeneous nodes and a complex network environment is enhanced.

The invention discloses a heterogeneous split learning collaborative optimization method and system based on a feature alignment adapter, and belongs to the edge computing and distributed machine learning technology. Based on an edge splitting learning framework, each client constructs a personalized local underlying model according to local computing resources and data distribution characteristics, and performs cooperative training with a top model shared by the server. According to the method, a feature adapter module is introduced into a model splitting layer to align heterogeneous intermediate feature representations from different clients, so that training obstacles caused by inconsistency of a model architecture and output dimensions are eliminated. The method further comprises the steps of constructing an initialization mechanism to accelerate convergence of splitting training, including initialization of an adapter and a feature distillation-based heterogeneous client model initialization strategy, and further jointly optimizing the server model, the adapter and the heterogeneous client model based on a global small-batch stochastic gradient descent algorithm, so as to improve the resolution of the heterogeneous client model. And the split model accuracy and the training efficiency under the equipment resource and data heterogeneous environment are effectively improved.

The invention discloses a method for reducing large model illusion of a question answering system based on knowledge graph representation learning, which comprises the following steps of: firstly, based on Ollivier-Ricci curvature calculation, embedding different structures in a knowledge graph into a plurality of approximate geometric spaces so as to analyze geometric patterns in data; secondly, generating a new entity representation by aggregating neighbor information, and transmitting and fusing information in different geometric spaces by using indexes; and finally, a dynamic curvature adaptive adjustment strategy is adopted to promote cooperative training and efficient fusion of multi-geometric space representation. According to the method, a complex semantic relationship is accurately described by combining geometric space and knowledge graph representation learning, geometric distortion in an embedding process is effectively reduced, and a structured knowledge system is constructed. According to the method, the accuracy and credibility of a large model in knowledge reasoning are improved, the illusion problem caused by incomplete or contradictory knowledge representation is solved, and powerful support is provided for a question and answer system.

The invention discloses a voltage sag state estimation method and system based on complex task decomposition reasoning, and the method comprises the steps: firstly solving actual problems in a physical world through guiding LLM by employing a mathematical tool, and giving text output fitting the reality through combining the mathematical tool and the feedback of a thinking tree architecture simulating human thinking behaviors; in order to solve the practical problem of the physical world, the invention constructs a physically-driven DGCN model, and the output of a mathematical tool better fits the practical situation by combining a multi-layer graph convolution layer and taking a power grid topology connection relationship as a physical constraint condition. And finally, introducing an improved DGCN model based on cooperative training and cross-power-grid regional knowledge migration, and improving the overall generalization and adaptability of LLM when voltage sag state estimation is carried out on unknown power grid topology.

The invention relates to the technical field of intelligent operation of a power distribution network, and particularly discloses a power distribution network load prediction and scheduling system based on multi-modal data and semi-modal learning, and the system comprises the steps: dividing monitoring nodes into label nodes and label-free nodes through collecting the multi-modal data of the power distribution network in real time; constructing a power distribution network causal knowledge base containing load type and meteorological condition causal association by using the labeled node data; causal association is used as priori knowledge to configure a semi-supervised meta-learning model comprising a main prediction network and a knowledge query network; carrying out cooperative training by taking the labeled data as a supervision signal and taking the unlabeled data as a supplement; in the prediction stage, the initial load prediction of the main prediction network and the target causal association retrieved by the knowledge query network are combined, and physical rule conformity correction is achieved through calculation; and performing multi-level security check based on the corrected load prediction result, and automatically executing a hierarchical scheduling strategy according to the early warning level.

The invention discloses a federal multitask learning method and system for hepatocellular carcinoma. The invention belongs to the technical field of distributed machine learning and privacy computing, and is suitable for a scene of cross-device or cross-organization multi-client collaborative training differentiated machine learning models on the premise of data privacy protection. The technical problems to be solved are that in a multi-task federated learning scene, due to large local data difference of different clients, the performance of a client model is poor, and convergence difficulty is possibly caused by the client model which only depends on gradient aggregation to execute different tasks. According to the method, channel selection based on language guidance is adopted, and the client model learns specific domain knowledge by using predefined text prompts; and realizing personalized updating of the client model by adopting parameter aggregation based on language guidance and applying a hierarchical cross attention mechanism. The method is mainly used for tasks of tumor segmentation, early recurrence prediction, survival analysis and the like aiming at hepatocellular carcinoma.

A method may comprise: training, by a network node, a first machine learning (ML) model for selection of training modes for collaborative training of a second ML model by a plurality of devices, wherein the first ML model is configured for selection of the training modes based on radio channel state information (CSI) of the devices and an estimate of energy consumption for training the second ML model by a respective device of the devices; transmitting the first ML model to the devices; transmitting the second ML model to the devices; receiving radio CSI from each of the devices; sharing the received radio CSI with the devices; receiving, from the devices, indications of the training modes of the devices for the collaborative training of the second ML model; and performing iterative training of the second ML model.

The invention discloses an urban traffic cooperative scheduling method and system based on a large model and multiple agents. The method comprises the steps that natural language task description is converted into a task semantic graph and a structured prompt; generating a plurality of strategy candidates by using a large language model, and performing language scoring and reasoning arbitration; based on the scoring result, selecting an optimal strategy for multi-agent execution; behavior execution data are collected and evaluated and fed back, and strategy closed-loop updating is achieved through parameter optimization and Prompt fine tuning; and a distributed task embedding mechanism and a lightweight migration module are combined, so that the multi-task adaptability and the training efficiency of the system are improved. The cooperative training system constructed by the invention has the capabilities of natural language interaction, strategy interpretability, behavior controllability and task migration, and is suitable for various complex urban tasks such as traffic jam dispersion, emergency response, signal lamp linkage and the like.