5008 results about "Privacy protection" patented technology

The present invention provides a facial recognition system using deep learning methodologies while integrating privacy-preserving capabilities. This system employs convolutional neural networks (CNNs) to extract and classify facial features, ensuring high accuracy in recognition tasks. Moreover, the system addresses privacy concerns by incorporating techniques such as facial feature encryption and anonymization, thereby enhancing user privacy and data security. This invention is applicable across various domains, including security, surveillance, access control, and personalized services, where facial recognition is utilized while preserving individual privacy.

The invention provides a time sequence knowledge graph federation collaborative optimization method, system and device based on causal inference and a storage medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: generating an enhanced knowledge unit with a causal mark through the real-time access of a multi-field heterogeneous data stream and the execution of a space-time alignment operation; by calculating new and old knowledge conflict scores, conflict resolution and version management are realized through a decision tree mechanism, and a time sequence knowledge graph with history tracing is output; node weights are dynamically distributed among distributed nodes based on knowledge entropy, a hierarchical aggregation strategy is adopted to update an entity embedding layer and a relation prediction layer, and a global optimization model is output; the method comprises the following steps: analyzing a natural language query containing an anti-fact condition, extracting a factor sub-graph from a time sequence knowledge graph, executing intervention calculation, and generating an anti-fact influence report, thereby solving the technical problems of a traditional time sequence knowledge graph in the aspects of multi-source heterogeneous data fusion, knowledge conflict resolution and privacy protection; and the accuracy and the interpretability of the knowledge graph are improved.

The invention relates to a multi-modal public opinion risk early warning system and method based on a dynamic knowledge graph and federal reinforcement learning, and belongs to the technical field of public opinion analysis. The system comprises a data acquisition module, a modal fusion module, a knowledge graph construction module, a comparative learning module, a federal reinforcement learning modeling module and a response output module. The system is based on multi-source heterogeneous data, multi-modal semantic alignment of texts, images, videos and the like is achieved, entity relations and propagation paths are mined through a dynamically updated knowledge graph, collaborative modeling under privacy protection among terminals is achieved by fusing federal reinforcement learning, and then real-time sensing, level early warning and multi-level response strategy recommendation of public opinion risks are achieved. Based on the system, the method has the advantages of high fusion precision, high response speed and strong visual propagation path, and is widely applied to the fields of enterprise crisis management, government affair and public opinion monitoring and public safety.

A platform for coordinating networks of specialized AI agents that enables secure collaboration through token-based communication and real-time result streaming. The system features a central orchestration engine managing interactions between domain-specific expert agents, with memory management and optional encryption for secure data handling. The platform uses efficient communication protocols for knowledge compression and faster reasoning, while a standardized agent interface system handles security, privacy, and policy requirements. It scales across distributed computing environments to enable complex collaborative tasks like personalized content creation, materials discovery, and drug development while optimizing resource usage and maintaining data privacy.

The invention belongs to the field of intelligent edge systems and privacy enhancement computing, and particularly relates to a privacy protection-oriented robot large model cloud edge end collaborative reasoning and federated learning system, which comprises a cloud server layer used for deploying a large-scale pre-training model and executing complex reasoning and global federated learning coordination; the edge calculation layer is used for deploying an intermediate layer model and executing local data aggregation, privacy protection processing and intermediate feature calculation; the terminal equipment layer is used for deploying a lightweight model and executing data acquisition, primary processing and lightweight reasoning; the federated learning framework is used for optimizing the model; the privacy protection module is used for integrating data localization, differential privacy, homomorphic encryption, secure multi-party computing and a block chain verification mechanism; the adaptive allocation module is used for dynamically adjusting computing resources. According to the method, the problems of privacy leakage risk, computing resource limitation, network delay, insufficient data isolation and the like of the traditional AI service in a robot scene are solved, and efficient privacy protection and data security isolation are realized.

A distributed intelligent authentication method based on dynamic multi-modal fusion relates to the field of network security, and adopts an alliance chain + DAG hybrid block chain architecture, combines a threshold signature to realize secret key fragment management, and switches among PBFT, Raft and probabilistic algorithms through a dynamic consensus mechanism to improve authentication efficiency. The multi-mode authentication module is based on a dynamic weight distribution algorithm, integrates biological characteristics, behavior analysis, equipment fingerprints and environmental factors, and combines an LSTM-GAN model and a quantum random number driven challenge-response mechanism to realize zero-trust verification under environmental perception. The session management module generates a session key by using a chaotic mapping algorithm. In the aspect of privacy protection, CKKS homomorphic encryption, zero-knowledge proof and attribute-based encryption are fused. According to the method, the block chain technology, the secure multi-party computing technology, the machine learning technology and the quantum cryptography technology are fused, and a high-performance, high-security and strong-privacy-protection distributed authentication solution is provided.

The invention discloses a digital asset secure transaction system and method based on a block chain, and belongs to the cross field of block chain technology and financial science and technology. The system adopts a layered architecture design and comprises an intelligent contract execution layer, a distributed account book storage layer and a cross-chain interaction layer. The transaction method comprises the following steps: generating a digital identity certificate based on asymmetric encryption, and realizing privacy protection through zero-knowledge proof; a secure transaction channel is constructed by adopting a multi-signature mechanism, and sensitive data processing is performed in combination with a trusted execution environment; and designing a dynamic fragmentation strategy to optimize the transaction throughput, and establishing an on-chain and off-chain collaborative verification mechanism. The innovation point is that a double-layer verification model combining a verifiable delay function and a threshold signature is provided, and millisecond-level confirmation is realized while the transaction irreversibility is ensured. The system supports multi-chain asset atomic exchange, and interoperability of different block chain networks is realized through a heterogeneous cross-chain gateway. The scheme has the characteristics of high transaction confirmation speed, high privacy protection level and high system expansibility.

The invention discloses a multi-modal data real-time identification and cooperative processing system based on edge computing and federated learning. The multi-modal data real-time identification and cooperative processing system comprises a cloud center coordination node, a plurality of edge computing nodes, a cross-modal encryption engine, a federated learning controller and a model updating verification module. The cloud center coordination node executes federated learning model aggregation and dynamic task allocation, and generates a cross-modal encryption strategy; and the edge computing node is configured with a multi-modal data acquisition module, a local model training unit and a co-processing gateway to realize multi-modal data acquisition and local processing. The system encrypts vision, acoustics and text data by using differentiated algorithms such as spatial confusion, frequency domain permutation and homomorphic encryption; the federated learning controller carries out multi-modal feature fusion, hierarchical encryption and dynamic networking at the edge node; and the model updating verification module performs aggregation updating after ensuring parameter consistency by using secure multi-party calculation. According to the method, real-time processing and privacy protection of multi-modal data are realized, and the data co-processing efficiency is improved.

The invention provides an industrial control network security service security guarantee system based on behavior analysis, which belongs to the technical field of industrial control network security, and comprises a multi-source data fusion acquisition module, a dynamic behavior modeling engine, a federal learning analysis cluster, an attack chain prediction module, a self-adaptive protection strategy executor and a model evolution feedback ring, wherein the multi-source data fusion acquisition module synchronously acquires industrial control network flow (including OPC UA / Modbus / DNP3 protocol analysis), equipment operation logs, user operation behavior fingerprints and physical interface state data, and the physical interface state data comprises electrical characteristic fluctuation monitoring of USB / network interfaces. According to the scheme, through multi-technology fusion and closed-loop design, the problems of static performance, single-dimension analysis defects and response lag of a traditional industrial control security scheme are effectively solved, a comprehensive protection system with dynamic modeling, intelligent decision making, privacy protection and continuous optimization is constructed, and the security and service reliability of an industrial control network are remarkably improved.

The invention, which relates to the technical field of network awareness anomaly detection, discloses a network awareness anomaly detection system based on big data, comprising a data acquisition module, a feature fusion module, a map construction module, a model calculation module, a root cause reasoning module, a threshold decision module and a response control module. The data acquisition module receives network flow data, equipment state data and system log data and outputs a standardized feature set; the feature fusion module is connected with the data acquisition module, dynamically calculates a weight coefficient of each data source based on information entropy, performs feature aggregation of privacy protection through a federated learning framework, and outputs a fusion feature vector; the atlas construction module is connected with the feature fusion module, maintains a network equipment node set and a communication edge set in real time, and updates a space-time association atlas according to a topology change event; and the model calculation module is connected with the atlas construction module, extracts topological features through a space-time diagram convolutional network, and updates a detection model based on an incremental learning mechanism.

The invention relates to the technical field of agricultural information management, and particularly discloses an agricultural information management system and method based on a big data platform, and the method comprises the steps: firstly deploying a multi-source data collection module at an edge calculation node, and obtaining and standardizing the soil moisture content, meteorological environment and equipment operation data in real time; secondly, constructing a local dynamic irrigation strategy model, and realizing multi-objective optimization through a reinforcement learning algorithm; establishing a federated learning framework at the cloud, dynamically distributing node weights by adopting an attention mechanism, and realizing model aggregation of privacy protection in combination with secure multi-party computing; an optimal irrigation instruction is generated through a multi-source data fusion engine, and a three-level response exception handling mechanism is established; and finally, a closed-loop feedback system containing short-term incremental learning and long-term architecture optimization is formed. The corresponding management system comprises six functional modules, namely a data acquisition module, a local modeling module, a federated learning module, a real-time decision-making module, an abnormal monitoring module and a closed-loop optimization module.

The invention relates to the technical field of power station operation and maintenance, and discloses a power generation equipment intelligent operation and maintenance method and system based on model autonomous selection, and the method comprises the steps: obtaining the multi-mode operation and maintenance data of a photovoltaic power station, and generating a multi-mode operation and maintenance data set; inputting each modal data of the multi-modal operation and maintenance data set into a corresponding module for feature extraction; inputting the extracted feature vectors into a contrast learning network for cross-modal alignment, and outputting an executable decision result by combining a retrieval enhancement generation technology with a knowledge graph; and constructing a privacy protection training framework through federated learning, inputting an executable decision result into a digital twin system for strategy verification, and generating a trained multi-modal large model for the photovoltaic power station to select a corresponding module in the trained multi-modal large model based on the feature data for real-time monitoring. According to the invention, the problems of poor accuracy and untimely reaction in the traditional operation and maintenance process of the photovoltaic power station are solved, and the operation and maintenance of the power station can be carried out timely and accurately.

The invention discloses a hospital information intelligent analysis and decision-making system based on a multi-modal large model, and relates to the technical field of hospital information analysis and decision-making. The system comprises a data acquisition module, a preprocessing and fusion module, a large model construction and training module, an intelligent analysis module, a decision support module, a knowledge graph construction and application module, a data security and privacy protection module, a system evaluation and optimization module, a multi-hospital cooperation and data sharing module, a mobile application module and the like, and all the modules cooperate to realize hospital information intelligent processing and decision making. The system integrates multi-modal data, assists in precise diagnosis, recommends a treatment scheme, predicts resource demands, monitors medical quality, assists medical research, manages patient health and the like, comprehensively improves the intelligent level of hospitals, optimizes medical services and benefits doctors and patients.

The invention provides an intelligent scheduling and control method and device for an integrated energy system. According to the method, power, gas and heat resource operation data are acquired, multi-scale layered modeling is performed according to a time scale and a space scale, and a power resource state space model, a gas resource flow continuity model, a heat resource heat balance model and a multi-energy coupling characteristic constraint model are established; carrying out feature extraction and dimension reduction representation by adopting a deep auto-encoder network; cooperative training of multiple groups of cognitive models is carried out through a split hierarchical federal learning framework, and a global intelligent model is obtained; constructing a neural architecture search network with a hybrid bionic learning rule, setting a hierarchical scheduling target, and generating a hierarchical intelligent scheduling strategy; and a fault-tolerant control mechanism is constructed, and error detection and correction of operation deviation are realized. According to the invention, multi-time scale collaboration, collaborative learning under multi-device group privacy protection and high-reliability fault-tolerant control are realized, and the operation efficiency and reliability of the integrated energy system are remarkably improved.

The invention relates to the related technical field of digital data processing, in particular to a personalized federated learning method and system for a heterogeneous multi-source industrial internet, and the method comprises the steps: connecting a client, evaluating a load, time delay and modal similarity to generate a dynamic association table, deploying a hierarchical encryption protocol, and constructing a priority queue; a cache mechanism is set to coordinate distributed iterative optimization, so that the technical problem that network oscillation and computing resource waste are aggravated due to overhigh load of part of nodes caused by frequent access and exit of equipment and data volume difference in the industrial internet and repeated migration of clients and nodes is caused is solved, cross-equipment shared knowledge base vectors are extracted, and the computing efficiency is improved. The technical effects of reducing the influence of model isomerism on aggregation, dynamically scheduling high-frequency parameter local aggregation and low-frequency parameter cloud synchronization, optimizing the association weight of a client and a fog node in real time, realizing privacy protection and efficient personalized federated learning, and ensuring the privacy and security of user data in the training process are achieved.

The invention provides a dynamic routing selection method and system, electronic equipment and a storage medium, and aims to solve the problem that a routing strategy is difficult to adapt to a dynamically changing network, the method comprises the following steps: a terminal layer collects the state of a terminal and network data, and performs lightweight feature extraction; the edge node layer receives the data of the terminal layer, carries out space-time-semantic feature aggregation, and generates a region-level resource scheduling and routing decision strategy based on fragmented reinforcement learning; the central cloud service layer gathers whole network data, generates a global optimization strategy and issues the global optimization strategy; the edge node layer fuses global optimization and a region-level strategy, and executes dynamic routing selection; and security and privacy protection are provided through the trusted chain layer. According to the invention, adaptive path selection can be realized, the network resource utilization rate is improved, and the network stability is improved.

The invention relates to the technical field of intelligent customer service control, and discloses a federated learning driven customer service robot cooperative control method and system. The method comprises the following steps: deploying a local intention recognition model at a plurality of nodes, collecting a user dialogue stream, extracting a semantic behavior track fragment, and generating a behavior feature vector set containing a time sequence and context association; the federal cooperative controller performs periodic aggregation, constructs a cross-node feature alignment mapping table based on trajectory similarity, and generates a global behavior feature distribution map; calculating node feature offset, screening high-contribution-degree nodes in combination with a sparse activation threshold, and allocating aggregation tasks; a knowledge distillation compression model is used at the high-contribution-degree nodes, weight updating parameters are extracted, compensation coefficients are added, and an encrypted updating package is generated; and the federal cooperative controller carries out heterogeneous fusion on the encrypted packet, reconstructs a global intention decision tree and carries out segmentation and distribution, so that efficient cooperation and optimization are realized, and privacy protection and service adaptability are considered.

The invention discloses an industrial equipment fault prediction system based on federated learning and a privacy protection method, and relates to the field of industrial equipment fault prediction. The data acquisition preprocessing module extracts fault features through compressed sensing downsampling, screens and uploads the fault features; the federal learning training module adopts a layered architecture and a dynamic algorithm to schedule a learning rate; the fault prediction and diagnosis module constructs a space-time diagram neural network and fuses a physical model to improve generalization; the privacy protection security communication module performs homomorphic encryption storage and zero-knowledge proof verification update; the knowledge graph construction reasoning module constructs a dynamic graph, locates a fault root cause through causal reasoning, and supports cross-device knowledge migration. By adopting the quantum and federated learning technology, the industrial equipment fault diagnosis accuracy is high, the attack resistance is high, the encryption efficiency is greatly improved, the model training time is shortened, cross-equipment knowledge migration is realized, the operation and maintenance cost is reduced, and the intelligent operation and maintenance development of the industrial equipment is promoted.

The invention relates to a federated learning system based on multi-key homomorphic encryption and adaptive differential privacy, and belongs to the technical field of privacy computing. According to the system, on the premise that no trusted third party exists, a multi-client collaborative key generation and threshold decryption mechanism is achieved, it is ensured that model parameters are always in an encrypted state in the aggregation process, and leakage of a single node is prevented. By introducing a parameter sensitivity analysis and selective encryption strategy, the system only encrypts high-risk parameters, and the encryption burden is effectively reduced. Meanwhile, in combination with an adaptive privacy budget allocation mechanism, the system dynamically adjusts noise intensity according to a model training state, and model performance and convergence speed are maintained while privacy protection capability is improved. According to the method, high robustness and collusion resistance are realized, stable operation under the condition that part of clients are offline is supported, and the method is suitable for application scenes such as medical treatment and finance with high data sensitivity and strict performance requirements.

The invention discloses a privacy protection system based on federated learning, and relates to the technical field of privacy protection. The system comprises a distributed participating node cluster, wherein each participating node is configured with a local model training unit and a privacy protection module; the coordination server is connected with each participating node through a secure communication layer and comprises a model aggregation module and a dynamic trust evaluation module; the global model distribution channel is used for broadcasting the encrypted global model parameters to participating nodes; and the privacy protection module is integrated in a local participating node and comprises a homomorphic encryption engine and a local differential privacy injector. According to the method, privacy protection strength improvement, model utility optimization, system efficiency breakthrough and security boundary expansion are synchronously achieved under a federated learning framework, and an industrial-grade solution is provided for cross-domain data collaborative learning.

The invention discloses an Internet of Vehicles communication resource allocation method based on federal multi-agent deep reinforcement learning, and belongs to the technical field of deep crossing of intelligent transportation, wireless communication and distributed machine learning. According to the technical scheme, in an Internet of Vehicles scene, a vehicle is taken as an agent, and local training is performed through a multi-agent depth deterministic strategy gradient algorithm: each agent decides spectrum access, power control and bandwidth allocation based on a local state; asynchronous federation learning is adopted, each agent uploads local model parameters to a global server, weights are dynamically adjusted based on model updating quality, communication quality and updating frequency, and a global model is generated through weighted aggregation; and issuing the updated global model to an intelligent agent to realize distributed privacy protection and dynamic optimization of communication resources. The method has the beneficial effects that the spectrum efficiency, the transmission success rate and the system adaptability are remarkably improved on the premise of ensuring the data security, and the method has wide application prospects and commercial values.

The invention discloses an accounting data checking method and system based on artificial intelligence, and the method comprises the steps: extracting multi-modal accounting data from a distributed tax data source through a federated learning framework, carrying out the anonymization aggregation of the data through a differential privacy technology, and generating a privacy-protected joint feature vector; inputting the joint feature vector into a causal reasoning model, identifying an abnormal fluctuation mode in the accounting data through anti-fact analysis, and outputting an abnormal index set with causal association; performing traceability reasoning on the abnormal index set by using a dynamic time sequence knowledge graph, generating a cross-cycle risk conduction path, and positioning a risk source entity; and generating an explainable inspection decision tree based on the risk source entity, dynamically adjusting an early warning threshold through adaptive threshold optimization, and outputting a graded early warning signal and a targeted inspection scheme. According to the embodiment of the invention, the accuracy, interpretability and risk traceability of distributed tax inspection can be improved.

The invention relates to the field of industrial intelligent control, in particular to a real-time production efficiency optimization system based on an artificial intelligence technology, and the system comprises an equipment fault prediction module which is used for collecting multi-modal time sequence data, and predicting the equipment fault probability based on a deep learning network; the process parameter adjusting and optimizing module is in communication connection with the equipment fault prediction module and is used for receiving the fault prediction probability and dynamically adjusting process parameters based on a reinforcement learning algorithm; the federated learning and incremental training module is in communication connection with the equipment fault prediction module and the process parameter tuning module, and is used for realizing collaborative optimization and privacy protection of a local model and a global model, greatly improving the fault prediction accuracy, reducing the false alarm rate from 20% to 5% or below, prolonging a prediction window from 10 minutes to 30 minutes or above, and improving the prediction efficiency. A sufficient preventive maintenance time window is provided for a production system; and the process parameter optimization effect is remarkably improved, and the comprehensive efficiency improvement space of the equipment is expanded to 8-12% from the traditional 5%.

The invention belongs to the technical field of medical artificial intelligence, and discloses an artificial intelligence-based auxiliary diagnosis and treatment system, which comprises a multi-modal data acquisition module, a dynamic learning module, a diagnosis reasoning module, a privacy protection module, an interactive decision module and an early warning monitoring module, the output end of the multi-modal data acquisition module is connected with the input end of the privacy protection module, the output end of the privacy protection module is connected with the input end of the dynamic learning module, the output end of the dynamic learning module is connected with the input end of the diagnostic reasoning module, and the output end of the diagnostic reasoning module is connected with the input end of the interactive decision module. And the early warning monitoring module monitors abnormal data in real time and performs bidirectional interaction with the diagnosis reasoning module. According to the method, multi-source medical data are integrated, and high-precision real-time auxiliary diagnosis is realized by adopting a dynamic incremental learning and privacy encryption technology; the medical worker cooperation efficiency is improved through an interactive interface, the safety is guaranteed in combination with real-time monitoring and early warning, and the system can remarkably improve the diagnosis and treatment efficiency and accuracy.

The invention discloses a federated learning-driven cross-domain supply chain elastic inventory optimization system and a method thereof, and aims at realizing inventory data collaboration among same-level enterprises or regional nodes through transverse federated learning and ensuring data security by adopting a self-adaptive differential privacy protection mechanism. The method comprises the steps of constructing a transverse federated learning network, locally performing data preprocessing, adding differential privacy noise, iteratively training a global model based on federated deep reinforcement learning, generating a transverse inventory allocation and replenishment decision, and performing model adaptive adjustment in real time based on key performance indicators. According to the method, multi-target balance is considered, inventory configuration is dynamically optimized through a multi-target reward function, the inventory turnover rate is remarkably increased, the inventory holding cost is reduced, the service level is improved, and the method is suitable for various scenes such as retail chain, manufacturing industry distributed storage and cross-regional logistics distribution; and global optimal inventory configuration is realized on the premise of ensuring data privacy.

The invention provides a cross-platform user behavior data intelligent aggregation and analysis processing method and system, and relates to the technical field of data processing, and the method comprises the steps: obtaining multi-platform user historical behavior data, carrying out the time sequence sorting, carrying out the session segmentation based on the time sequence data, obtaining a behavior session sequence, and constructing a behavior migration probability graph; an intention recognition agent and a behavior prediction agent are deployed through a decentralized federal reinforcement learning framework, cross-platform cooperative training is realized by using a differential privacy mechanism, and anti-factual reasoning is performed based on a multi-layer causal relationship graph to generate a user intention portrait, so that the cross-platform data analysis accuracy is improved, and the privacy protection capability is enhanced.

The invention relates to the technical field of federated learning, and discloses a federated learning contribution degree adaptive evaluation method and system based on dynamic differential privacy, and the method comprises the steps: obtaining a local model gradient generated by a federated learning participant during local training, and calculating the data sensitivity of the federated learning participant. The central server allocates a privacy budget to federal learning participants according to a historical contribution fluctuation coefficient and the data sensitivity; generating corresponding noise according to the allocated privacy budget, and adding the noise into the local model gradient; and the aggregation server calculates the current contribution degree of the federated learning participant according to the local model gradient after noise addition, and fuses the current contribution degree and the historical contribution degree of the federated learning participant by using a time attenuation factor to obtain the final contribution degree. According to the method, the problems of one-sided evaluation and insufficient privacy protection in a traditional method are solved, and collaborative improvement of federated learning in privacy protection and contribution degree evaluation accuracy is realized.

The invention relates to the technical field of distributed learning, in particular to a large model pre-training system based on distributed parallel processing. In the system, a data distribution layer collects a node resource state through a fragmentation module and generates a dynamic scheduling strategy; a computing resource layer configuration model initialization module and a strategy switching module support flexible switching of multiple modes such as tensor parallelism, data parallelism and assembly line parallelism; the network communication management layer is combined with topology perception and gradient compression technologies, so that the communication efficiency is improved; and the model aggregation layer realizes global parameter updating and training tuning under privacy protection through a security aggregation and optimization control mechanism. All layers of the system operate cooperatively, the calculation efficiency, the communication performance and the data security of large model pre-training can be effectively improved, and the method is suitable for model development and deployment in a large-scale heterogeneous calculation environment.

The invention discloses an edge cloud hierarchical collaborative task unloading optimization method fused with federal learning. According to the method, a three-level collaborative architecture of a terminal equipment layer, an edge node layer and a cloud center layer is constructed, and a task is split into a sub-task set containing a dependency relationship through a task analysis module. The edge node adopts a mixed model of a graph attention network and a double-delay depth deterministic strategy gradient, generates an unloading proportion in combination with an edge cloud topology and a real-time resource state, and realizes differential privacy protection through Laplace noise. In the federal learning process, the edge node only uploads Parel encryption parameters after local training, and the cloud generates a global meta-model through secret sharing aggregation and issues and updates the global meta-model. The multi-objective optimization model takes time delay, energy consumption and communication traffic as objectives, dynamically adjusts weight coefficients, and forms a'unloading-execution-learning-optimization 'closed loop. The method gives consideration to privacy protection and real-time performance, and is suitable for scenes with high requirements for privacy and timeliness.

The invention discloses a federated learning backdoor defense method based on pruning and fine tuning in the technical field of artificial intelligence and network security, the method realizes defense through two core mechanisms of dynamic pruning and gradient constraint fine tuning, and the method comprises the following steps: firstly, calculating a sensitivity score based on a neuron activation frequency and a weight outlier degree; dynamically identifying and cutting redundant neurons utilized by a backdoor, and blocking an abnormal activation path; secondly, gradient direction consistency detection and amplitude constraint are introduced in the fine tuning stage, and a malicious client is inhibited from reconstructing a back door through an abnormal gradient; the server continuously purifies model parameters and enhances robustness by cyclically executing pruning, fine tuning and aggregation operations; the method does not need to depend on an extra clean data set, strictly follows a federated learning privacy protection principle, reduces communication overhead through lightweight pruning, maintains main task performance in combination with gradient constraint, is suitable for a federated learning scene in which edge equipment participates, and effectively balances a defense effect and model stability.