333 results about "Model aggregation" patented technology

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 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 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 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 AI oT-based cloud network side-end federated learning system, and relates to the technical field of cloud network side-end federated learning, comprising the combination of Internet of Things, cloud computing and artificial intelligence technology, and through intelligent cooperative computing of cloud-side-end three-layer computing resources, the cloud network side-end federated learning is realized. The AI oT-based cloud network side-end federated learning system further comprises a cloud module, a network module, an edge module and a terminal module, and has the advantages that local model aggregation is firstly carried out on edge nodes, a part of calculation tasks can be completed at an edge end, a model result subjected to primary processing and aggregation is uploaded to the cloud, and the cloud network side-end federated learning is realized. Different edge nodes and terminal devices have different computing capabilities, the cloud end dynamically distributes training tasks to match tasks with different complexity degrees with devices with corresponding computing capabilities, and the edge nodes with strong computing capabilities can undertake more complex model training sub-tasks. And the terminal equipment with relatively weak computing power is responsible for some simple tasks such as data preprocessing and the like.

The invention discloses a multi-source threat intelligence privacy fusion processing method and system, and relates to the technical field of network security threat intelligence analysis and processing, and the method comprises the steps: collecting threat intelligence, and carrying out the cleaning, standardization and desensitization; establishing a threat index semantic model and aligning cross-modal features; the central coordination module completes model aggregation after organization node local training; and based on the aggregation model fusion intelligence, semantic association and attack link reconstruction are established, and a result is generated and fed back for optimization. The technical problems that in cross-organization fusion processing of multi-source heterogeneous threat intelligence, data formats are not uniform, and privacy protection and intelligence collaborative analysis contradictions exist, so that threat detection is not comprehensive and inaccurate, and accurate evaluation and effective management and control requirements are difficult to meet are solved. The technical effects that the multi-source heterogeneous threat intelligence is effectively fused on the premise of privacy protection, the comprehensiveness and accuracy of threat detection are improved, and the requirements for accurate assessment and effective management and control of threats in a cross-organization scene are met are achieved.

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

Disclosed in the present application is a blockchain-based distributed ensemble learning method. On the basis of a designed three-layer blockchain structure, which consists of a mini block, an ensemble block and a key block, by means of a consensus protocol, a miner in a network is enabled to train a base model on a training set after performing sampling with replacement, and aggregates models from other miners; and finally, information of the base model and information of an ensemble model are recorded in a blockchain, such that the whole process of model training, model ensemble and model evaluation is fused into a blockchain consensus, and the whole ensemble learning process is automatically executed in a blockchain network. In this way, the present application can increase the utilization rate of the computing power in a blockchain network by means of a proof-of-useful-work mechanism based on machine learning, and a center node is prevented from being introduced during a model aggregation process, thereby maximizing the degree of decentralization of a blockchain.

The invention belongs to the technical field of block chains and federated learning, and particularly discloses a dynamic incentive federated learning method based on a trusted execution environment and a block chain. The method comprises the following steps: firstly, constructing a core computing security area by utilizing TEE, and executing key links such as model aggregation, client screening and contribution measurement in a hardware isolated trusted environment; and then, a block chain and an intelligent contract technology are adopted as a decentralized trust root to realize effective management of identities of participants, tamper-proof records of key certificates and automatic distribution of economic incentives. Secondly, establishing a set of dynamic excitation and reputation mechanism executed in the TEE, and performing credible quantification and automatic reward on the contribution of the client based on multi-dimensional indexes such as model quality, historical reputation, asset pledge, participation stability and the like; and finally, end-to-end data encryption is realized through a session key mechanism in the TEE, so that the data privacy of the client is effectively protected.

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

The invention discloses an MEC federated learning model aggregation optimization method based on non-independent identically distributed data, and relates to the technical field of edge computing, and the method comprises the following steps: a distributed mobile terminal collects local data, records performance parameters, carries out power law distribution classification after preprocessing, sets a dynamic threshold value, and optimizes local training by using a dynamic sampling algorithm; the asynchronous transmission global model is fused with weight optimization updating and timestamp monitoring technologies to provide heterogeneous network state feedback, and efficient federated learning model aggregation is achieved. According to the method, the efficiency and precision of MEC federated learning under non-independent identically distributed data are remarkably improved, resource allocation is effectively balanced through dynamic threshold adjustment and sampling optimization, the communication overhead is reduced, the model generalization ability is enhanced through asynchronous model updating and weight fusion strategies, real-time feedback of the network state is ensured through the timestamp monitoring technology, and the method has the advantages of being high in reliability and high in reliability. And an efficient and reliable solution is provided for federated learning in the field of edge computing.

The invention discloses a violation snapshot method based on image recognition, which relates to the related technical field of traffic video monitoring and comprises the steps of environment perception, scene analysis, scene feature library establishment, multi-modal data acquisition, server model aggregation, model issuing and fine adjustment and violation behavior recognition. The invention further discloses a violation snapshot system based on image recognition. The violation snapshot system comprises an environment sensing module, a data acquisition module, a data processing center, a violation behavior recognition module and a snapshot and recording module. Different from a fixed snapshot strategy of a traditional method, the system can dynamically adjust the snapshot strategy according to real-time understanding of a traffic scene, the system can automatically improve the snapshot frame rate when detecting a high-risk scene in which a traffic accident is about to occur, and the snapshot frame rate can be automatically increased in a road section in which the traffic flow is small and the scene is simple. Snapshot resource consumption can be properly reduced; therefore, a complex and changeable traffic environment can be better dealt with, and the snapshot effectiveness and the resource utilization efficiency are improved.

The invention provides a personalized federal learning method suitable for an edge network, which is characterized in that a collaborative training network topology between clients is constructed based on a graph, and local models of the clients and aggregation relationships thereof are described as points and edges of the graph. Secondly, a personalized model aggregation mode based on group dynamics is established, a personalized aggregation matrix is obtained according to the adjacency matrix and the aggregation weight matrix, and finally, the personalized aggregation matrix is used for weighting personalized model parameters uploaded by all clients to obtain a personalized aggregation model; the adjacency matrix is used for controlling the personalized model of the client to move towards the personalized models of other clients aggregated by the client in the state space; the aggregation weight matrix is used for controlling the moving degree of the personalized model; therefore, the personalized model aggregation is rapidly realized in the edge server, and the personalized model automatically completes clustering in the convergence movement in the state space along with the continuous proceeding of training and aggregation, so that the collaborative training of the personalized model between clients is realized.

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

The invention discloses an underwater edge privacy protection task unloading method based on differential federated learning. Firstly, sensor nodes are arranged in a target water area, a network architecture is constructed, a state space, an action space and a reward function are defined respectively, and the sensor nodes execute local model training after sensing environment data. Then, each sensor node uploads local model parameters to a sea surface buoy for model aggregation, and differential disturbance is introduced in the uploading process to protect privacy; and after model aggregation is completed, the sea surface buoy issues a global model to each sensor node, and the node generates a task unloading strategy according to the global model. And finally, the sensor node encrypts the task data through homomorphic encryption, and safely unloads the encrypted calculation task to the corresponding sea surface buoy or low orbit satellite for execution. According to the method, privacy leakage in an underwater task unloading process is effectively reduced, and the security of data transmission in an underwater network is improved.

The invention relates to the technical field of the Internet of Things, in particular to a lightweight distributed model aggregation method applied to a low-power-consumption chip of the Internet of Things, and is suitable for an Internet of Things system composed of a cloud server and a plurality of edge nodes. Each edge node learns an optimal strategy from offline data, and meanwhile, a complex teacher model is compressed into a lightweight student model through a knowledge distillation technology, so that the local calculation burden is reduced. And each edge node uploads quantized and sparse model parameters to the cloud server, and the cloud server performs federal aggregation by adopting a weighted average method to generate a global model and feeds back the global model to each terminal on the premise of ensuring data security. In order to adapt to the practical application of a low-power-consumption chip, a dynamic voltage frequency regulation technology is introduced in the hardware level, and the energy consumption and the communication overhead are effectively reduced. According to the method, the model training process of the edge nodes of the Internet of Things is optimized, and federated learning of privacy protection, efficient communication and cooperative self-adaption is realized.

The invention discloses a federal self-supervised learning backdoor attack method and system based on illusion positive example entanglement, and the method comprises the steps: setting a target of an attacker to embed a backdoor into an image encoder during a pre-training period, and guaranteeing that a downstream classifier trained on the image encoder predicts an image with a trigger as a target class; the limited poisoning data is fully utilized, and the feature similarity of the same trigger sample is improved through the illusion enhancement module, so that the malicious client can effectively embed the backdoor into the global encoder. In the training process, a forged positive sample is generated, learning of the model in a feature space is misguided intentionally, poisoning data is utilized to the maximum extent, an entanglement loss function is designed to optimize a target, the attack efficiency is kept in the whole model aggregation process, and the attenuation risk is reduced. According to the invention, a new perspective and important challenge can be provided for the security research of the federated learning system, and the improvement and development of defense capability are promoted.

The invention discloses a heterogeneous data financial intelligent model collaborative construction device and method and related equipment, and relates to the technical field of model processing, and an institution grouping device of a public control unit clusters and groups financial institutions participating in model construction by using clustering, data distribution speculation and other related technologies. The mechanisms of the same type in the grouping result have similar data distribution characteristics, and the mechanisms in the same group have data with complementary distribution characteristics, that is, missing data knowledge is mutually compensated, and finally good training data with the same distribution are formed on the whole. The grouping result is input into a first model scheduler to schedule mechanism selection of local training of the federal model, the model is guided to complete training and learn complete data knowledge in different grouped mechanisms, and a financial intelligent model meeting result expectation is output through multiple rounds of local model training, correct model scheduling and model aggregation. The method solves the influence of heterogeneous data, and constructs a financial intelligent model with high accuracy.

The present invention belongs to the technical field of communication networks. Disclosed is an adaptive split federated learning method for a distributed AI training service in a 6G computing power network. The method comprises: establishing a 6G computing power network model, and constructing an adaptive split federated learning model for a distributed AI training service; analyzing the delays of each intelligent terminal in a model training stage and a model aggregation stage; constructing a total service delay minimization optimization problem; and using a shortest-path search algorithm to perform joint optimization on model segmentation modes, cooperative node selection modes and multi-domain resource scheduling modes in the model training stage and the model aggregation stage. In the present invention, by means of joint optimization of a model segmentation mode, node selection and multi-dimensional resource allocation in a multi-base-station scenario, the problem of network congestion caused by a single-base-station learning framework processing the aggregation of a large number of intelligent terminal models is alleviated, and the present invention adapts to the heterogeneous computing power of intelligent terminals and the differentiated channel quality in an actual network, thereby improving the efficiency and generalization capability of model training.

The invention relates to the technical field of network security, in particular to an encrypted network abnormal traffic detection method, system and device and a medium. According to the invention, by combining edge calculation and federated learning, the efficiency and precision of encrypted network traffic anomaly detection are improved. The edge device independently trains a local model and uploads the local model to the central server, and the central server aggregates model parameters through federal learning to generate a global detection model. The method can be closer to a data source for real-time analysis, and delay and bandwidth consumption caused by data transmission are reduced. Meanwhile, by means of local training and model aggregation, concentrated storage and transmission of sensitive data are avoided, and data privacy is effectively guaranteed. The global model is optimized for multiple rounds, abnormal behaviors in encrypted traffic can be accurately identified, the network security protection capability is improved, and an efficient and accurate network security solution with privacy protection is provided.

The invention relates to the field of edge computing scheduling, in particular to a task scheduling method and system based on edge computing, and the method comprises the steps: carrying out the optimization of multi-scale knowledge distillation and weighted model aggregation on a local task model of each edge node through employing a dynamic knowledge transfer mechanism, and updating a global model; an energy consumption perception strategy optimization module is introduced, a Markov decision process model is constructed, the model is optimized by using a reinforcement learning algorithm, and system performance and energy consumption are dynamically balanced; inputting a historical task load into the pre-training model, and predicting a future load through a self-supervised learning single-mode time sequence prediction module; and optimizing the Markov decision process model according to the future load predicted in the previous step, and dynamically adjusting the computing resource allocation of the edge node. The method has the beneficial effects that the task scheduling efficiency, the task precision and the real-time response capability are improved, the importance of efficient nodes is ensured, the generalization capability and the stability of a global model are improved, and the method has good robustness.

The invention provides a data space construction method and system based on dynamic ontology modeling and privacy calculation, and belongs to the technical field of data processing. The method comprises the following steps: respectively constructing a structured semantic network and a vector space of multi-modal data based on the multi-modal data, aligning nodes of the semantic network with the vector space by using an attention mechanism to generate a joint knowledge representation, compressing the joint knowledge representation into a low-dimensional knowledge representation, and constructing a low-dimensional index, modeling by adopting a federated learning improved algorithm combined with differential privacy to obtain an original model; distributing the initial model and the initial model parameters to each participant for federated learning training, and performing model aggregation by using the trained encrypted model parameters to obtain a global model; and finally, distributing model parameters of the global model to each participant for model deployment to form a three-level collaborative data space. According to the invention, the risk of privacy disclosure is reduced, and the storage overhead of the data space is reduced.

The invention discloses a streaming media adaptive transmission method and system based on reinforcement learning and electronic equipment, and the method comprises the steps: obtaining current streaming media state data, obtaining and sending streaming media control information through an online reinforcement learning model, then obtaining latest streaming media state data, and calculating a model award to continuously update the model; adopting a federated learning method to aggregate the models of all streaming media clients to obtain a new global model; the system comprises a sending end module, a receiving end module, a forward error correction module, a network state detection module, a video quality evaluation module, a model aggregation module and a reinforcement learning agent module used for adaptively adjusting audio and video coding and forward error correction algorithm parameters. The method is suitable for various heterogeneous weak network environments, can effectively deal with the problems of occasionality, complexity and the like of the network, realizes self-adaptive transmission of audios and videos, and remarkably improves the user experience quality.

The invention relates to a modal heterogeneous federated learning privacy protection method based on a cross-modal prototype, and belongs to the technical field of artificial intelligence and information security. The server firstly initializes a global model for each client, wherein the global model comprises a mapping module of a corresponding mode of the client; and the server sends the initialized model to the corresponding client. The multi-mode client initializes a private prototype learning model. And local model training is carried out by using the global model, the single-mode client and the multi-mode client. And after the local prototype and the local model sent by the client are received, the server executes prototype aggregation and model aggregation respectively. And after receiving the global prototype pair and the global mapping module, the client updates the local mapping module and starts a new round of training until a specific number of training rounds is reached. According to the method and the device, the communication overhead can be reduced, local models are not required to have the same structure, and a single-mode client can acquire information from a missing mode prototype in a targeted manner.

The invention provides an equipment fault prediction method based on federated learning. According to the method, the weight adaptability of the fault data in the aggregation process under different distribution dimensions is guaranteed by dynamically adjusting the aggregation weight. According to the method, firstly, a contribution degree mechanism is put forward, then the contribution degree of each client and the weight of a data set are combined for model aggregation, and a FedAvg-ContribData algorithm is put forward. Experimental data show that compared with FedAvg and FedAvg-Data algorithms, the method disclosed by the invention has the advantages that the accuracy is improved by 39.67% and 7.87% in the aspect of accuracy; in the aspect of accuracy, the accuracy is improved by 40.43% and 9.06%; in the aspect of recall rate, the recall rate is increased by 41.00% and 7.86%; and in the aspect of the F1 coefficient, the F1 coefficient is increased by 46.62% and 9.34%.

The invention discloses a personalized federal learning method based on homomorphic encryption, which comprises the following steps of: firstly, introducing constraint between a global model and a personalized model through a regularization method, adopting a separated updating strategy, performing multi-round updating on the personalized model by a client by utilizing local data, and performing multi-round updating on the personalized model by utilizing local data; and the personalized model parameters are uploaded to a server. Meanwhile, an optimization strategy is provided, namely, the strategy of dynamically adjusting the aggregation weight by utilizing the precision and frequency of each client on the public verification set is used, so that the global aggregation weight is dynamically adjusted, and the global model is updated more fairly and accurately. Besides, a model pool is introduced to realize preferential updating of a local model of the client, dynamic weight adjustment and model pool selection are both lightweight operations, a fully homomorphic encryption technology is adopted to realize ciphertext model aggregation, original precision / frequency data and model parameters of the client are ensured to be encrypted in the whole process in transmission and calculation, and gradient inversion attacks can be effectively defended.

The invention discloses a federated learning client scheduling method, device and system with contribution perception, and the method comprises the steps: quantizing the marginal contribution of a client into an information entropy change amount caused after a model parameter is updated and added into a client alliance based on an information entropy theory and a Shapley Value contribution evaluation method; according to the client selection method based on the dobby machine theory, under the constraint of availability and fairness, the utilization of known high-contribution clients and the exploration of unknown clients are balanced. The system comprises a server unit responsible for contribution evaluation, client selection and model aggregation; and the client unit is responsible for local model training. The method, the device and the system can be used for federal learning scenes with heterogeneous clients, and performance of a final global model is improved while participation fairness of the clients is guaranteed.

The invention discloses a federal learning efficient training optimization method for a 6G network, and the method comprises the steps: transmitting a global model parameter to a client through a parameter server in a downlink communication stage, and transmitting the global model parameter to the client through a local training stage; according to the customized local training strategy, the convergence speed is improved by training the specified number of iterations at a local client and adding constraint terms in a target function, so that the total number of rounds of communication is reduced; in the uplink communication stage, part of clients participate in clients with relatively high rule selection calculation efficiency and upload training results of the clients at the same time, so that the communication cost of each round is reduced; in the model aggregation stage, a parameter server distributes reasonable weights for different local parameters based on a weighted aggregation strategy of data quality so as to improve model precision, and finally outputs optimized global model parameters so as to realize efficient model training and parameter updating, and an efficient and reliable solution is provided for distributed learning in a 6G network.

The invention provides a small signal frequency domain impedance automatic construction method compatible with a network tracking type system and a network constructing type system. For a complex system connected with a network-following type or network-constructing type converter, a uniform state-space equation is used for representing the characteristics of the converter, and an impedance calculation framework based on mapping between a time-frequency domain and a continuous discrete domain is established. The implementation process comprises the following steps: firstly, deducing a state-space equation of a single element, performing linear approximation and time domain discretization at a stable equilibrium point to form an equivalent Norton model, then dividing the whole network into two subsystems, hiding internal electrical parameters of the subsystems through model aggregation, and finally obtaining an equivalent Norton model; and automatic conversion from a small-signal time domain state space model of a single element to a system frequency domain model is realized. In the method, the system impedance model can be automatically constructed only according to branch and node information, the operation is simple, the expansibility is high, and the method is suitable for a large-scale power grid with a complex structure.

By combining a federated learning model and a low earth orbit (LEO) satellite network, the present invention provides a general federated learning framework over an LEO satellite network (FedSN) for achieving federated learning. The FedSN is composed of two main components: a sub-structure scheme and pseudo-synchronous model aggregation. The sub-structure scheme comprises sub-structure customization, distribution and aggregation methods, and respectively solves problems such as resource limitations, training imbalance, staleness of intra-group models. According to a pseudo-synchronous model aggregation strategy, the difference between weights of models is brought into a weight function, and a buffer-based aggregation method is developed, so that the staleness of inter-group models is reduced. Extensive experimental results show that a FedSN framework is superior to a state-of-the-art baseline. The present invention shows and improves the potential of deploying a FedSN on an LEO satellite network.