Deep Convolution Neural Networks(DCNNs)can capture discriminative features from large datasets.However,how to incrementally learn new samples without forgetting old ones and recognize novel classes that arise in the d...Deep Convolution Neural Networks(DCNNs)can capture discriminative features from large datasets.However,how to incrementally learn new samples without forgetting old ones and recognize novel classes that arise in the dynamically changing world,e.g.,classifying newly discovered fish species,remains an open problem.We address an even more challenging and realistic setting of this problem where new class samples are insufficient,i.e.,Few-Shot Class-Incremental Learning(FSCIL).Current FSCIL methods augment the training data to alleviate the overfitting of novel classes.By contrast,we propose Filter Bank Networks(FBNs)that augment the learnable filters to capture fine-detailed features for adapting to future new classes.In the forward pass,FBNs augment each convolutional filter to a virtual filter bank containing the canonical one,i.e.,itself,and multiple transformed versions.During back-propagation,FBNs explicitly stimulate fine-detailed features to emerge and collectively align all gradients of each filter bank to learn the canonical one.FBNs capture pattern variants that do not yet exist in the pretraining session,thus making it easy to incorporate new classes in the incremental learning phase.Moreover,FBNs introduce model-level prior knowledge to efficiently utilize the limited few-shot data.Extensive experiments on MNIST,CIFAR100,CUB200,andMini-ImageNet datasets show that FBNs consistently outperformthe baseline by a significantmargin,reporting new state-of-the-art FSCIL results.In addition,we contribute a challenging FSCIL benchmark,Fishshot1K,which contains 8261 underwater images covering 1000 ocean fish species.The code is included in the supplementary materials.展开更多
Background With the development of information technology,there is a significant increase in the number of network traffic logs mixed with various types of cyberattacks.Traditional intrusion detection systems(IDSs)are...Background With the development of information technology,there is a significant increase in the number of network traffic logs mixed with various types of cyberattacks.Traditional intrusion detection systems(IDSs)are limited in detecting new inconstant patterns and identifying malicious traffic traces in real time.Therefore,there is an urgent need to implement more effective intrusion detection technologies to protect computer security.Methods In this study,we designed a hybrid IDS by combining our incremental learning model(KANSOINN)and active learning to learn new log patterns and detect various network anomalies in real time.Conclusions Experimental results on the NSLKDD dataset showed that KAN-SOINN can be continuously improved and effectively detect malicious logs.Meanwhile,comparative experiments proved that using a hybrid query strategy in active learning can improve the model learning efficiency.展开更多
Recently, deep convolutional neural networks (DCNNs) have achieved remarkable results in image classification tasks. Despite convolutional networks’ great successes, their training process relies on a large amount of...Recently, deep convolutional neural networks (DCNNs) have achieved remarkable results in image classification tasks. Despite convolutional networks’ great successes, their training process relies on a large amount of data prepared in advance, which is often challenging in real-world applications, such as streaming data and concept drift. For this reason, incremental learning (continual learning) has attracted increasing attention from scholars. However, incremental learning is associated with the challenge of catastrophic forgetting: the performance on previous tasks drastically degrades after learning a new task. In this paper, we propose a new strategy to alleviate catastrophic forgetting when neural networks are trained in continual domains. Specifically, two components are applied: data translation based on transfer learning and knowledge distillation. The former translates a portion of new data to reconstruct the partial data distribution of the old domain. The latter uses an old model as a teacher to guide a new model. The experimental results on three datasets have shown that our work can effectively alleviate catastrophic forgetting by a combination of the two methods aforementioned.展开更多
This article presents hourly load forecasting by using an incremental learning model called Online Sequential Extreme Learning Machine(OSELM),which can learn and adapt automatically according to new arrival input.Howe...This article presents hourly load forecasting by using an incremental learning model called Online Sequential Extreme Learning Machine(OSELM),which can learn and adapt automatically according to new arrival input.However,the use of OS-ELM requires a sufficient amount of initial training sample data,which makes OS-ELM inoperable if sufficiently accurate sample data cannot be obtained.To solve this problem,a synthesis of the initial training sample is proposed.The synthesis of the initial sample is achieved by taking the first data received at the start of working and adding random noises to that data to create new and sufficient samples.Then the synthesis samples are used to initial train the OS-ELM.This proposed method is compared with Fully Online Extreme Learning Machine(FOS-ELM),which is an incremental learning model that also does not require the initial training samples.Both the proposed method and FOS-ELM are used for hourly load forecasting from the Hourly Energy Consumption dataset.Experiments have shown that the proposed method with a wide range of noise levels,can forecast hourly load more accurately than the FOS-ELM.展开更多
At this current time,data stream classification plays a key role in big data analytics due to its enormous growth.Most of the existing classification methods used ensemble learning,which is trustworthy but these metho...At this current time,data stream classification plays a key role in big data analytics due to its enormous growth.Most of the existing classification methods used ensemble learning,which is trustworthy but these methods are not effective to face the issues of learning from imbalanced big data,it also supposes that all data are pre-classified.Another weakness of current methods is that it takes a long evaluation time when the target data stream contains a high number of features.The main objective of this research is to develop a new method for incremental learning based on the proposed ant lion fuzzy-generative adversarial network model.The proposed model is implemented in spark architecture.For each data stream,the class output is computed at slave nodes by training a generative adversarial network with the back propagation error based on fuzzy bound computation.This method overcomes the limitations of existing methods as it can classify data streams that are slightly or completely unlabeled data and providing high scalability and efficiency.The results show that the proposed model outperforms stateof-the-art performance in terms of accuracy(0.861)precision(0.9328)and minimal MSE(0.0416).展开更多
Intrusion detection is a hot field in the direction of network security.Classical intrusion detection systems are usually based on supervised machine learning models.These offline-trained models usually have better pe...Intrusion detection is a hot field in the direction of network security.Classical intrusion detection systems are usually based on supervised machine learning models.These offline-trained models usually have better performance in the initial stages of system construction.However,due to the diversity and rapid development of intrusion techniques,the trained models are often difficult to detect new attacks.In addition,very little noisy data in the training process often has a considerable impact on the performance of the intrusion detection system.This paper proposes an intrusion detection system based on active incremental learning with the adaptive capability to solve these problems.IDS consists of two modules,namely the improved incremental stacking ensemble learning detection method called Multi-Stacking model and the active learning query module.The stacking model can cope well with concept drift due to the diversity and generalization selection of its base classifiers,but the accuracy does not meet the requirements.The Multi-Stacking model improves the accuracy of the model by adding a voting layer on the basis of the original stacking.The active learning query module improves the detection of known attacks through the committee algorithm,and the improved KNN algorithm can better help detect unknown attacks.We have tested the latest industrial IoT dataset with satisfactory results.展开更多
The visions of Industry 4.0 and 5.0 have reinforced the industrial environment.They have also made artificial intelligence incorporated as a major facilitator.Diagnosing machine faults has become a solid foundation fo...The visions of Industry 4.0 and 5.0 have reinforced the industrial environment.They have also made artificial intelligence incorporated as a major facilitator.Diagnosing machine faults has become a solid foundation for automatically recognizing machine failure,and thus timely maintenance can ensure safe operations.Transfer learning is a promising solution that can enhance the machine fault diagnosis model by borrowing pre-trained knowledge from the source model and applying it to the target model,which typically involves two datasets.In response to the availability of multiple datasets,this paper proposes using selective and adaptive incremental transfer learning(SA-ITL),which fuses three algorithms,namely,the hybrid selective algorithm,the transferability enhancement algorithm,and the incremental transfer learning algorithm.It is a selective algorithm that enables selecting and ordering appropriate datasets for transfer learning and selecting useful knowledge to avoid negative transfer.The algorithm also adaptively adjusts the portion of training data to balance the learning rate and training time.The proposed algorithm is evaluated and analyzed using ten benchmark datasets.Compared with other algorithms from existing works,SA-ITL improves the accuracy of all datasets.Ablation studies present the accuracy enhancements of the SA-ITL,including the hybrid selective algorithm(1.22%-3.82%),transferability enhancement algorithm(1.91%-4.15%),and incremental transfer learning algorithm(0.605%-2.68%).These also show the benefits of enhancing the target model with heterogeneous image datasets that widen the range of domain selection between source and target domains.展开更多
Performance pattern identification is the key basis for fault detection and condition prediction,which plays a major role in ensuring safety and reliability in complex electromechanical systems(CESs).However,there are...Performance pattern identification is the key basis for fault detection and condition prediction,which plays a major role in ensuring safety and reliability in complex electromechanical systems(CESs).However,there are a few problems related to the automatic and adaptive updating of an identification model.Aiming to solve the problem of identification model updating,a novel framework for performance pattern identification of the CESs based on the artificial immune systems and incremental learning is proposed in this paper to classify real-time monitoring data into different performance patterns.First,an unsupervised clustering technique is used to construct an initial identification model.Second,the artificial immune and outlier detection algorithms are applied to identify abnormal data and determine the type of immune response.Third,incremental learning is employed to trace the dynamic changes of patterns,and operations such as pattern insertion,pattern removal,and pattern revision are designed to realize automatic and adaptive updates of an identification model.The effectiveness of the proposed framework is demonstrated through experiments with the benchmark and actual pattern identification applications.As an unsupervised and self-adapting approach,the proposed framework inherits the preponderances of the conventional methods but overcomes some of their drawbacks because the retraining process is not required in perceiving the pattern changes.Therefore,this method can be flexibly and efficiently used for performance pattern identification of the CESs.Moreover,the proposed method provides a foundation for fault detection and condition prediction,and can be used in other engineering applications.展开更多
Online incremental learning is one of the emerging research interests among the researchers in the recent years.The sentiment classification through the online incremental learning faces many challenges due to the lim...Online incremental learning is one of the emerging research interests among the researchers in the recent years.The sentiment classification through the online incremental learning faces many challenges due to the limitations in the memory and the computing resources available for processing the online reviews.This work has introduced an online incremental learning algorithm for classifying the train reviews.The sentiments available in the reviews provided for the public services are necessary for improving the quality of the service.This work proposes the online kernel optimizationbased support vector machine(OKO-SVM)classifier for the sentiment classification of the train reviews.This paper is the extension of the previous work kernel optimizationbased support vector machine(KO-SVM).The OKO-SVM classifier uses the proposed fuzzy bound for modifying the weight for each incoming review database for the particular time duration.The simulation uses the standard train review and the movie review database for the classification.From the simulation results,it is evident that the proposed model has achieved a better performance with the values of 84.42%,93.86%,and 74.56%regarding the accuracy,sensitivity,and specificity while classifying the train review database.展开更多
This paper develops a fully data-driven,missingdata tolerant method for post-fault short-term voltage stability(STVS)assessment of power systems against the incomplete PMU measurements.The super-resolution perception(...This paper develops a fully data-driven,missingdata tolerant method for post-fault short-term voltage stability(STVS)assessment of power systems against the incomplete PMU measurements.The super-resolution perception(SRP),based on a deep residual learning convolutional neural network,is employed to cope with the missing PMU measurements.The incremental broad learning(BL)is used to rapidly update the model to maintain and enhance the online application performance.Being different from the state-of-the-art methods,the proposed method is fully data-driven and can fill up missing data under any PMU placement information loss and network topology change scenario.Simulation results demonstrate that the proposed method has the best performance in terms of STVS assessment accuracy and missing-data tolerance among the existing methods on the benchmark testing system.展开更多
Since its inception,the Internet has been rapidly evolving.With the advancement of science and technology and the explosive growth of the population,the demand for the Internet has been on the rise.Many applications i...Since its inception,the Internet has been rapidly evolving.With the advancement of science and technology and the explosive growth of the population,the demand for the Internet has been on the rise.Many applications in education,healthcare,entertainment,science,and more are being increasingly deployed based on the internet.Concurrently,malicious threats on the internet are on the rise as well.Distributed Denial of Service(DDoS)attacks are among the most common and dangerous threats on the internet today.The scale and complexity of DDoS attacks are constantly growing.Intrusion Detection Systems(IDS)have been deployed and have demonstrated their effectiveness in defense against those threats.In addition,the research of Machine Learning(ML)and Deep Learning(DL)in IDS has gained effective results and significant attention.However,one of the challenges when applying ML and DL techniques in intrusion detection is the identification of unknown attacks.These attacks,which are not encountered during the system’s training,can lead to misclassification with significant errors.In this research,we focused on addressing the issue of Unknown Attack Detection,combining two methods:Spatial Location Constraint Prototype Loss(SLCPL)and Fuzzy C-Means(FCM).With the proposed method,we achieved promising results compared to traditional methods.The proposed method demonstrates a very high accuracy of up to 99.8%with a low false positive rate for known attacks on the Intrusion Detection Evaluation Dataset(CICIDS2017)dataset.Particularly,the accuracy is also very high,reaching 99.7%,and the precision goes up to 99.9%for unknown DDoS attacks on the DDoS Evaluation Dataset(CICDDoS2019)dataset.The success of the proposed method is due to the combination of SLCPL,an advanced Open-Set Recognition(OSR)technique,and FCM,a traditional yet highly applicable clustering technique.This has yielded a novel method in the field of unknown attack detection.This further expands the trend of applying DL and ML techniques in the development of intrusion detection systems and cybersecurity.Finally,implementing the proposed method in real-world systems can enhance the security capabilities against increasingly complex threats on computer networks.展开更多
The available modelling data shortage issue makes it difficult to guarantee the performance of data-driven building energy prediction(BEP)models for both the newly built buildings and existing information-poor buildin...The available modelling data shortage issue makes it difficult to guarantee the performance of data-driven building energy prediction(BEP)models for both the newly built buildings and existing information-poor buildings.Both knowledge transfer learning(KTL)and data incremental learning(DIL)can address the data shortage issue of such buildings.For new building scenarios with continuous data accumulation,the performance of BEP models has not been fully investigated considering the data accumulation dynamics.DIL,which can learn dynamic features from accumulated data adapting to the developing trend of new building time-series data and extend BEP model's knowledge,has been rarely studied.Previous studies have shown that the performance of KTL models trained with fixed data can be further improved in scenarios with dynamically changing data.Hence,this study proposes an improved transfer learning cross-BEP strategy continuously updated using the coarse data incremental(CDI)manner.The hybrid KTL-DIL strategy(LSTM-DANN-CDI)uses domain adversarial neural network(DANN)for KLT and long short-term memory(LSTM)as the Baseline BEP model.Performance evaluation is conducted to systematically qualify the effectiveness and applicability of KTL and improved KTL-DIL.Real-world data from six-type 36 buildings of six types are adopted to evaluate the performance of KTL and KTL-DIL in data-driven BEP tasks considering factors like the model increment time interval,the available target and source building data volumes.Compared with LSTM,results indicate that KTL(LSTM-DANN)and the proposed KTL-DIL(LSTM-DANN-CDI)can significantly improve the BEP performance for new buildings with limited data.Compared with the pure KTL strategy LSTM-DANN,the improved KTL-DIL strategy LSTM-DANN-CDI has better prediction performance with an average performance improvement ratio of 60%.展开更多
Nowadays,activities of daily living(ADL)recognition system has been considered an important field of computer vision.Wearable and optical sensors are widely used to assess the daily living activities in healthy people...Nowadays,activities of daily living(ADL)recognition system has been considered an important field of computer vision.Wearable and optical sensors are widely used to assess the daily living activities in healthy people and people with certain disorders.Although conventional ADL utilizes RGB optical sensors but an RGB-D camera with features of identifying depth(distance information)and visual cues has greatly enhanced the performance of activity recognition.In this paper,an RGB-D-based ADL recognition system has been presented.Initially,human silhouette has been extracted from the noisy background of RGB and depth images to track human movement in a scene.Based on these silhouettes,full body features and point based features have been extracted which are further optimized with probability based incremental learning(PBIL)algorithm.Finally,random forest classifier has been used to classify activities into different categories.The n-fold crossvalidation scheme has been used to measure the viability of the proposed model on the RGBD-AC benchmark dataset and has achieved an accuracy of 92.71%over other state-of-the-art methodologies.展开更多
Many chronic disease prediction methods have been proposed to predict or evaluate diabetes through artificial neural network.However,due to the complexity of the human body,there are still many challenges to face in t...Many chronic disease prediction methods have been proposed to predict or evaluate diabetes through artificial neural network.However,due to the complexity of the human body,there are still many challenges to face in that process.One of them is how to make the neural network prediction model continuously adapt and learn disease data of different patients,online.This paper presents a novel chronic disease prediction system based on an incremental deep neural network.The propensity of users suffering from chronic diseases can continuously be evaluated in an incremental manner.With time,the system can predict diabetes more and more accurately by processing the feedback information.Many diabetes prediction studies are based on a common dataset,the Pima Indians diabetes dataset,which has only eight input attributes.In order to determine the correlation between the pathological characteristics of diabetic patients and their daily living resources,we have established an in-depth cooperation with a hospital.A Chinese diabetes dataset with 575 diabetics was created.Users’data collected by different sensors were used to train the network model.We evaluated our system using a real-world diabetes dataset to confirm its effectiveness.The experimental results show that the proposed system can not only continuously monitor the users,but also give early warning of physiological data that may indicate future diabetic ailments.展开更多
The invention of Phasor Measurement Units(PMUs)produce synchronized phasor measurements with high resolution real time monitoring and control of power system in smart grids that make possible.PMUs are used in transmit...The invention of Phasor Measurement Units(PMUs)produce synchronized phasor measurements with high resolution real time monitoring and control of power system in smart grids that make possible.PMUs are used in transmitting data to Phasor Data Concentrators(PDC)placed in control centers for monitoring purpose.A primary concern of system operators in control centers is maintaining safe and efficient operation of the power grid.This can be achieved by continuous monitoring of the PMU data that contains both normal and abnormal data.The normal data indicates the normal behavior of the grid whereas the abnormal data indicates fault or abnormal conditions in power grid.As a result,detecting anomalies/abnormal conditions in the fast flowing PMU data that reflects the status of the power system is critical.A novel methodology for detecting and categorizing abnormalities in streaming PMU data is presented in this paper.The proposed method consists of three modules namely,offline Gaussian Mixture Model(GMM),online GMM for identifying anomalies and clustering ensemble model for classifying the anomalies.The significant features of the proposed method are detecting anomalies while taking into account of multivariate nature of the PMU dataset,adapting to concept drift in the flowing PMU data without retraining the existing model unnecessarily and classifying the anomalies.The proposed model is implemented in Python and the testing results prove that the proposed model is well suited for detection and classification of anomalies on the fly.展开更多
The objective of this study is to construct a multi-department symptom-based automatic diagnosis model.However,it is dificult to establish a model to classify plenty of diseases and collect thousands of disease-sympto...The objective of this study is to construct a multi-department symptom-based automatic diagnosis model.However,it is dificult to establish a model to classify plenty of diseases and collect thousands of disease-symptom datasets simultaneously.Inspired by the thought of"knowledge graph is model",this study proposes to build an experience-infused knowledge model by continuously learning the experiential knowledge from data,and incrementally injecting it into the knowledge graph.Therefore,incremental learning and injection are used to solve the data collection problem,and the knowledge graph is modeled and containerized to solve the large-scale multi-classification problems.First,an entity linking method is designed and a heterogeneous knowledge graph is constructed by graph fusion.Then,an adaptive neural network model is constructed for each dataset,and the data is used for statistical initialization and model training.Finally,the weights and biases of the learned neural network model are updated to the knowledge graph.It is worth noting that for the incremental process,we consider both the data and class increments.We evaluate the diagnostic effectiveness of the model on the current dataset and the anti-forgetting ability on the historical dataset after class increment on three public datasets.Compared with the classical model,the proposed model improves the diagnostic accuracy of the three datasets by 5%,2%,and 15%on average,respectively.Meanwhile,the model under incremental learning has a better ability to resist forgetting.展开更多
Humans are experiencing the inclusion of artificial agents in their lives,such as unmanned vehicles,service robots,voice assistants,and intelligent medical care.If the artificial agents cannot align with social values...Humans are experiencing the inclusion of artificial agents in their lives,such as unmanned vehicles,service robots,voice assistants,and intelligent medical care.If the artificial agents cannot align with social values or make ethical decisions,they may not meet the expectations of humans.Traditionally,an ethical decision-making framework is constructed by rule-based or statistical approaches.In this paper,we propose an ethical decision-making framework based on incremental ILP(Inductive Logic Programming),which can overcome the brittleness of rule-based approaches and little interpretability of statistical approaches.As the current incremental ILP makes it difficult to solve conflicts,we propose a novel ethical decision-making framework considering conflicts in this paper,which adopts our proposed incremental ILP system.The framework consists of two processes:the learning process and the deduction process.The first process records bottom clauses with their score functions and learns rules guided by the entailment and the score function.The second process obtains an ethical decision based on the rules.In an ethical scenario about chatbots for teenagers’mental health,we verify that our framework can learn ethical rules and make ethical decisions.Besides,we extract incremental ILP from the framework and compare it with the state-of-the-art ILP systems based on ASP(Answer Set Programming)focusing on conflict resolution.The results of comparisons show that our proposed system can generate better-quality rules than most other systems.展开更多
Based on data from the Jilin Water Diversion Tunnels from the Songhua River(China),an improved and real-time prediction method optimized by multi-algorithm for tunnel boring machine(TBM)cutter-head torque is presented...Based on data from the Jilin Water Diversion Tunnels from the Songhua River(China),an improved and real-time prediction method optimized by multi-algorithm for tunnel boring machine(TBM)cutter-head torque is presented.Firstly,a function excluding invalid and abnormal data is established to distinguish TBM operating state,and a feature selection method based on the SelectKBest algorithm is proposed.Accordingly,ten features that are most closely related to the cutter-head torque are selected as input variables,which,in descending order of influence,include the sum of motor torque,cutter-head power,sum of motor power,sum of motor current,advance rate,cutter-head pressure,total thrust force,penetration rate,cutter-head rotational velocity,and field penetration index.Secondly,a real-time cutterhead torque prediction model’s structure is developed,based on the bidirectional long short-term memory(BLSTM)network integrating the dropout algorithm to prevent overfitting.Then,an algorithm to optimize hyperparameters of model based on Bayesian and cross-validation is proposed.Early stopping and checkpoint algorithms are integrated to optimize the training process.Finally,a BLSTMbased real-time cutter-head torque prediction model is developed,which fully utilizes the previous time-series tunneling information.The mean absolute percentage error(MAPE)of the model in the verification section is 7.3%,implying that the presented model is suitable for real-time cutter-head torque prediction.Furthermore,an incremental learning method based on the above base model is introduced to improve the adaptability of the model during the TBM tunneling.Comparison of the prediction performance between the base and incremental learning models in the same tunneling section shows that:(1)the MAPE of the predicted results of the BLSTM-based real-time cutter-head torque prediction model remains below 10%,and both the coefficient of determination(R^(2))and correlation coefficient(r)between measured and predicted values exceed 0.95;and(2)the incremental learning method is suitable for realtime cutter-head torque prediction and can effectively improve the prediction accuracy and generalization capacity of the model during the excavation process.展开更多
In view of the incremental learning problem of manifold learning algorithm, an adaptive neighborhood incremental principal component analysis(PCA) and locality preserving projection(LPP) manifold learning algorithm is...In view of the incremental learning problem of manifold learning algorithm, an adaptive neighborhood incremental principal component analysis(PCA) and locality preserving projection(LPP) manifold learning algorithm is presented, and the incremental learning principle of algorithm is introduced. For incremental sample data, the adjacency and covariance matrices are incrementally updated by the existing samples; then the dimensionality reduction results of the incremental samples are estimated by the dimensionality reduction results of the existing samples; finally, the dimensionality reduction results of the incremental and existing samples are updated by subspace iteration method. The adaptive neighborhood incremental PCA-LPP manifold learning algorithm is applied to processing of gearbox fault signals. The dimensionality reduction results by incremental learning have very small error, compared with those by batch learning. Spatial aggregation of the incremental samples is basically stable, and fault identification rate is increased.展开更多
Wearable health monitoring is a crucial technical tool that offers early warning for chronic diseases due to its superior portability and low power consumption.However,most wearable health data is distributed across d...Wearable health monitoring is a crucial technical tool that offers early warning for chronic diseases due to its superior portability and low power consumption.However,most wearable health data is distributed across dfferent organizations,such as hospitals,research institutes,and companies,and can only be accessed by the owners of the data in compliance with data privacy regulations.The first challenge addressed in this paper is communicating in a privacy-preserving manner among different organizations.The second technical challenge is handling the dynamic expansion of the federation without model retraining.To address the first challenge,we propose a horizontal federated learning method called Federated Extremely Random Forest(FedERF).Its contribution-based splitting score computing mechanism significantly mitigates the impact of privacy protection constraints on model performance.Based on FedERF,we present a federated incremental learning method called Federated Incremental Extremely Random Forest(FedIERF)to address the second technical challenge.FedIERF introduces a hardness-driven weighting mechanism and an importance-based updating scheme to update the existing federated model incrementally.The experiments show that FedERF achieves comparable performance with non-federated methods,and FedIERF effectively addresses the dynamic expansion of the federation.This opens up opportunities for cooperation between different organizations in wearable health monitoring.展开更多
基金support from the Strategic Priority Research Program of the Chinese Academy of Sciences under Grant No.XDA27000000.
文摘Deep Convolution Neural Networks(DCNNs)can capture discriminative features from large datasets.However,how to incrementally learn new samples without forgetting old ones and recognize novel classes that arise in the dynamically changing world,e.g.,classifying newly discovered fish species,remains an open problem.We address an even more challenging and realistic setting of this problem where new class samples are insufficient,i.e.,Few-Shot Class-Incremental Learning(FSCIL).Current FSCIL methods augment the training data to alleviate the overfitting of novel classes.By contrast,we propose Filter Bank Networks(FBNs)that augment the learnable filters to capture fine-detailed features for adapting to future new classes.In the forward pass,FBNs augment each convolutional filter to a virtual filter bank containing the canonical one,i.e.,itself,and multiple transformed versions.During back-propagation,FBNs explicitly stimulate fine-detailed features to emerge and collectively align all gradients of each filter bank to learn the canonical one.FBNs capture pattern variants that do not yet exist in the pretraining session,thus making it easy to incorporate new classes in the incremental learning phase.Moreover,FBNs introduce model-level prior knowledge to efficiently utilize the limited few-shot data.Extensive experiments on MNIST,CIFAR100,CUB200,andMini-ImageNet datasets show that FBNs consistently outperformthe baseline by a significantmargin,reporting new state-of-the-art FSCIL results.In addition,we contribute a challenging FSCIL benchmark,Fishshot1K,which contains 8261 underwater images covering 1000 ocean fish species.The code is included in the supplementary materials.
基金Supported by SJTU-HUAWEI TECH Cybersecurity Innovation Lab。
文摘Background With the development of information technology,there is a significant increase in the number of network traffic logs mixed with various types of cyberattacks.Traditional intrusion detection systems(IDSs)are limited in detecting new inconstant patterns and identifying malicious traffic traces in real time.Therefore,there is an urgent need to implement more effective intrusion detection technologies to protect computer security.Methods In this study,we designed a hybrid IDS by combining our incremental learning model(KANSOINN)and active learning to learn new log patterns and detect various network anomalies in real time.Conclusions Experimental results on the NSLKDD dataset showed that KAN-SOINN can be continuously improved and effectively detect malicious logs.Meanwhile,comparative experiments proved that using a hybrid query strategy in active learning can improve the model learning efficiency.
文摘Recently, deep convolutional neural networks (DCNNs) have achieved remarkable results in image classification tasks. Despite convolutional networks’ great successes, their training process relies on a large amount of data prepared in advance, which is often challenging in real-world applications, such as streaming data and concept drift. For this reason, incremental learning (continual learning) has attracted increasing attention from scholars. However, incremental learning is associated with the challenge of catastrophic forgetting: the performance on previous tasks drastically degrades after learning a new task. In this paper, we propose a new strategy to alleviate catastrophic forgetting when neural networks are trained in continual domains. Specifically, two components are applied: data translation based on transfer learning and knowledge distillation. The former translates a portion of new data to reconstruct the partial data distribution of the old domain. The latter uses an old model as a teacher to guide a new model. The experimental results on three datasets have shown that our work can effectively alleviate catastrophic forgetting by a combination of the two methods aforementioned.
文摘This article presents hourly load forecasting by using an incremental learning model called Online Sequential Extreme Learning Machine(OSELM),which can learn and adapt automatically according to new arrival input.However,the use of OS-ELM requires a sufficient amount of initial training sample data,which makes OS-ELM inoperable if sufficiently accurate sample data cannot be obtained.To solve this problem,a synthesis of the initial training sample is proposed.The synthesis of the initial sample is achieved by taking the first data received at the start of working and adding random noises to that data to create new and sufficient samples.Then the synthesis samples are used to initial train the OS-ELM.This proposed method is compared with Fully Online Extreme Learning Machine(FOS-ELM),which is an incremental learning model that also does not require the initial training samples.Both the proposed method and FOS-ELM are used for hourly load forecasting from the Hourly Energy Consumption dataset.Experiments have shown that the proposed method with a wide range of noise levels,can forecast hourly load more accurately than the FOS-ELM.
基金Taif University Researchers Supporting Project Number(TURSP-2020/126),Taif University,Taif,Saudi Arabia.
文摘At this current time,data stream classification plays a key role in big data analytics due to its enormous growth.Most of the existing classification methods used ensemble learning,which is trustworthy but these methods are not effective to face the issues of learning from imbalanced big data,it also supposes that all data are pre-classified.Another weakness of current methods is that it takes a long evaluation time when the target data stream contains a high number of features.The main objective of this research is to develop a new method for incremental learning based on the proposed ant lion fuzzy-generative adversarial network model.The proposed model is implemented in spark architecture.For each data stream,the class output is computed at slave nodes by training a generative adversarial network with the back propagation error based on fuzzy bound computation.This method overcomes the limitations of existing methods as it can classify data streams that are slightly or completely unlabeled data and providing high scalability and efficiency.The results show that the proposed model outperforms stateof-the-art performance in terms of accuracy(0.861)precision(0.9328)and minimal MSE(0.0416).
基金sponsored by the National Natural Science Foundation of China under Grants 62271264,61972207,and 42175194the Project through the Priority Academic Program Development(PAPD)of Jiangsu Higher Education Institution.
文摘Intrusion detection is a hot field in the direction of network security.Classical intrusion detection systems are usually based on supervised machine learning models.These offline-trained models usually have better performance in the initial stages of system construction.However,due to the diversity and rapid development of intrusion techniques,the trained models are often difficult to detect new attacks.In addition,very little noisy data in the training process often has a considerable impact on the performance of the intrusion detection system.This paper proposes an intrusion detection system based on active incremental learning with the adaptive capability to solve these problems.IDS consists of two modules,namely the improved incremental stacking ensemble learning detection method called Multi-Stacking model and the active learning query module.The stacking model can cope well with concept drift due to the diversity and generalization selection of its base classifiers,but the accuracy does not meet the requirements.The Multi-Stacking model improves the accuracy of the model by adding a voting layer on the basis of the original stacking.The active learning query module improves the detection of known attacks through the committee algorithm,and the improved KNN algorithm can better help detect unknown attacks.We have tested the latest industrial IoT dataset with satisfactory results.
文摘The visions of Industry 4.0 and 5.0 have reinforced the industrial environment.They have also made artificial intelligence incorporated as a major facilitator.Diagnosing machine faults has become a solid foundation for automatically recognizing machine failure,and thus timely maintenance can ensure safe operations.Transfer learning is a promising solution that can enhance the machine fault diagnosis model by borrowing pre-trained knowledge from the source model and applying it to the target model,which typically involves two datasets.In response to the availability of multiple datasets,this paper proposes using selective and adaptive incremental transfer learning(SA-ITL),which fuses three algorithms,namely,the hybrid selective algorithm,the transferability enhancement algorithm,and the incremental transfer learning algorithm.It is a selective algorithm that enables selecting and ordering appropriate datasets for transfer learning and selecting useful knowledge to avoid negative transfer.The algorithm also adaptively adjusts the portion of training data to balance the learning rate and training time.The proposed algorithm is evaluated and analyzed using ten benchmark datasets.Compared with other algorithms from existing works,SA-ITL improves the accuracy of all datasets.Ablation studies present the accuracy enhancements of the SA-ITL,including the hybrid selective algorithm(1.22%-3.82%),transferability enhancement algorithm(1.91%-4.15%),and incremental transfer learning algorithm(0.605%-2.68%).These also show the benefits of enhancing the target model with heterogeneous image datasets that widen the range of domain selection between source and target domains.
基金supported in part by the National Key R&D Program of China(Grant No.2017YFF0210500)in part by China Postdoctoral Science Foundation(Grant No.2017M620446)
文摘Performance pattern identification is the key basis for fault detection and condition prediction,which plays a major role in ensuring safety and reliability in complex electromechanical systems(CESs).However,there are a few problems related to the automatic and adaptive updating of an identification model.Aiming to solve the problem of identification model updating,a novel framework for performance pattern identification of the CESs based on the artificial immune systems and incremental learning is proposed in this paper to classify real-time monitoring data into different performance patterns.First,an unsupervised clustering technique is used to construct an initial identification model.Second,the artificial immune and outlier detection algorithms are applied to identify abnormal data and determine the type of immune response.Third,incremental learning is employed to trace the dynamic changes of patterns,and operations such as pattern insertion,pattern removal,and pattern revision are designed to realize automatic and adaptive updates of an identification model.The effectiveness of the proposed framework is demonstrated through experiments with the benchmark and actual pattern identification applications.As an unsupervised and self-adapting approach,the proposed framework inherits the preponderances of the conventional methods but overcomes some of their drawbacks because the retraining process is not required in perceiving the pattern changes.Therefore,this method can be flexibly and efficiently used for performance pattern identification of the CESs.Moreover,the proposed method provides a foundation for fault detection and condition prediction,and can be used in other engineering applications.
文摘Online incremental learning is one of the emerging research interests among the researchers in the recent years.The sentiment classification through the online incremental learning faces many challenges due to the limitations in the memory and the computing resources available for processing the online reviews.This work has introduced an online incremental learning algorithm for classifying the train reviews.The sentiments available in the reviews provided for the public services are necessary for improving the quality of the service.This work proposes the online kernel optimizationbased support vector machine(OKO-SVM)classifier for the sentiment classification of the train reviews.This paper is the extension of the previous work kernel optimizationbased support vector machine(KO-SVM).The OKO-SVM classifier uses the proposed fuzzy bound for modifying the weight for each incoming review database for the particular time duration.The simulation uses the standard train review and the movie review database for the classification.From the simulation results,it is evident that the proposed model has achieved a better performance with the values of 84.42%,93.86%,and 74.56%regarding the accuracy,sensitivity,and specificity while classifying the train review database.
基金The work was supported in part by National Natural Science Foundation of China(51807009,71931003,72061147004).
文摘This paper develops a fully data-driven,missingdata tolerant method for post-fault short-term voltage stability(STVS)assessment of power systems against the incomplete PMU measurements.The super-resolution perception(SRP),based on a deep residual learning convolutional neural network,is employed to cope with the missing PMU measurements.The incremental broad learning(BL)is used to rapidly update the model to maintain and enhance the online application performance.Being different from the state-of-the-art methods,the proposed method is fully data-driven and can fill up missing data under any PMU placement information loss and network topology change scenario.Simulation results demonstrate that the proposed method has the best performance in terms of STVS assessment accuracy and missing-data tolerance among the existing methods on the benchmark testing system.
基金This research was partly supported by the National Science and Technology Council,Taiwan with Grant Numbers 112-2221-E-992-045,112-2221-E-992-057-MY3 and 112-2622-8-992-009-TD1.
文摘Since its inception,the Internet has been rapidly evolving.With the advancement of science and technology and the explosive growth of the population,the demand for the Internet has been on the rise.Many applications in education,healthcare,entertainment,science,and more are being increasingly deployed based on the internet.Concurrently,malicious threats on the internet are on the rise as well.Distributed Denial of Service(DDoS)attacks are among the most common and dangerous threats on the internet today.The scale and complexity of DDoS attacks are constantly growing.Intrusion Detection Systems(IDS)have been deployed and have demonstrated their effectiveness in defense against those threats.In addition,the research of Machine Learning(ML)and Deep Learning(DL)in IDS has gained effective results and significant attention.However,one of the challenges when applying ML and DL techniques in intrusion detection is the identification of unknown attacks.These attacks,which are not encountered during the system’s training,can lead to misclassification with significant errors.In this research,we focused on addressing the issue of Unknown Attack Detection,combining two methods:Spatial Location Constraint Prototype Loss(SLCPL)and Fuzzy C-Means(FCM).With the proposed method,we achieved promising results compared to traditional methods.The proposed method demonstrates a very high accuracy of up to 99.8%with a low false positive rate for known attacks on the Intrusion Detection Evaluation Dataset(CICIDS2017)dataset.Particularly,the accuracy is also very high,reaching 99.7%,and the precision goes up to 99.9%for unknown DDoS attacks on the DDoS Evaluation Dataset(CICDDoS2019)dataset.The success of the proposed method is due to the combination of SLCPL,an advanced Open-Set Recognition(OSR)technique,and FCM,a traditional yet highly applicable clustering technique.This has yielded a novel method in the field of unknown attack detection.This further expands the trend of applying DL and ML techniques in the development of intrusion detection systems and cybersecurity.Finally,implementing the proposed method in real-world systems can enhance the security capabilities against increasingly complex threats on computer networks.
基金jointly supported by the Opening Fund of Key Laboratory of Low-grade Energy Utilization Technologies and Systems of Ministry of Education of China(Chongqing University)(LLEUTS-202305)the Opening Fund of State Key Laboratory of Green Building in Western China(LSKF202316)+4 种基金the open Foundation of Anhui Province Key Laboratory of Intelligent Building and Building Energy-saving(IBES2022KF11)“The 14th Five-Year Plan”Hubei Provincial advantaged characteristic disciplines(groups)project of Wuhan University of Science and Technology(2023D0504,2023D0501)the National Natural Science Foundation of China(51906181)the 2021 Construction Technology Plan Project of Hubei Province(2021-83)the Science and Technology Project of Guizhou Province:Integrated Support of Guizhou[2023]General 393.
文摘The available modelling data shortage issue makes it difficult to guarantee the performance of data-driven building energy prediction(BEP)models for both the newly built buildings and existing information-poor buildings.Both knowledge transfer learning(KTL)and data incremental learning(DIL)can address the data shortage issue of such buildings.For new building scenarios with continuous data accumulation,the performance of BEP models has not been fully investigated considering the data accumulation dynamics.DIL,which can learn dynamic features from accumulated data adapting to the developing trend of new building time-series data and extend BEP model's knowledge,has been rarely studied.Previous studies have shown that the performance of KTL models trained with fixed data can be further improved in scenarios with dynamically changing data.Hence,this study proposes an improved transfer learning cross-BEP strategy continuously updated using the coarse data incremental(CDI)manner.The hybrid KTL-DIL strategy(LSTM-DANN-CDI)uses domain adversarial neural network(DANN)for KLT and long short-term memory(LSTM)as the Baseline BEP model.Performance evaluation is conducted to systematically qualify the effectiveness and applicability of KTL and improved KTL-DIL.Real-world data from six-type 36 buildings of six types are adopted to evaluate the performance of KTL and KTL-DIL in data-driven BEP tasks considering factors like the model increment time interval,the available target and source building data volumes.Compared with LSTM,results indicate that KTL(LSTM-DANN)and the proposed KTL-DIL(LSTM-DANN-CDI)can significantly improve the BEP performance for new buildings with limited data.Compared with the pure KTL strategy LSTM-DANN,the improved KTL-DIL strategy LSTM-DANN-CDI has better prediction performance with an average performance improvement ratio of 60%.
基金This research was supported by a grant(2021R1F1A1063634)of the Basic Science Research Program through the National Research Foundation(NRF)funded by the Ministry of Education,Republic of Korea.
文摘Nowadays,activities of daily living(ADL)recognition system has been considered an important field of computer vision.Wearable and optical sensors are widely used to assess the daily living activities in healthy people and people with certain disorders.Although conventional ADL utilizes RGB optical sensors but an RGB-D camera with features of identifying depth(distance information)and visual cues has greatly enhanced the performance of activity recognition.In this paper,an RGB-D-based ADL recognition system has been presented.Initially,human silhouette has been extracted from the noisy background of RGB and depth images to track human movement in a scene.Based on these silhouettes,full body features and point based features have been extracted which are further optimized with probability based incremental learning(PBIL)algorithm.Finally,random forest classifier has been used to classify activities into different categories.The n-fold crossvalidation scheme has been used to measure the viability of the proposed model on the RGBD-AC benchmark dataset and has achieved an accuracy of 92.71%over other state-of-the-art methodologies.
基金funding from the Humanities and Social Sciences Projects of the Ministry of Education(Grant No.18YJC760112,Bin Yang)the Social Science Fund of Jiangsu Province(Grant No.18YSD002,Bin Yang)Open Fund of Hunan Key Laboratory of Smart Roadway and Cooperative Vehicle Infrastructure Systems(Changsha University of Science and Technology)(Grant No.kfj180402,Lingyun Xiang).
文摘Many chronic disease prediction methods have been proposed to predict or evaluate diabetes through artificial neural network.However,due to the complexity of the human body,there are still many challenges to face in that process.One of them is how to make the neural network prediction model continuously adapt and learn disease data of different patients,online.This paper presents a novel chronic disease prediction system based on an incremental deep neural network.The propensity of users suffering from chronic diseases can continuously be evaluated in an incremental manner.With time,the system can predict diabetes more and more accurately by processing the feedback information.Many diabetes prediction studies are based on a common dataset,the Pima Indians diabetes dataset,which has only eight input attributes.In order to determine the correlation between the pathological characteristics of diabetic patients and their daily living resources,we have established an in-depth cooperation with a hospital.A Chinese diabetes dataset with 575 diabetics was created.Users’data collected by different sensors were used to train the network model.We evaluated our system using a real-world diabetes dataset to confirm its effectiveness.The experimental results show that the proposed system can not only continuously monitor the users,but also give early warning of physiological data that may indicate future diabetic ailments.
文摘The invention of Phasor Measurement Units(PMUs)produce synchronized phasor measurements with high resolution real time monitoring and control of power system in smart grids that make possible.PMUs are used in transmitting data to Phasor Data Concentrators(PDC)placed in control centers for monitoring purpose.A primary concern of system operators in control centers is maintaining safe and efficient operation of the power grid.This can be achieved by continuous monitoring of the PMU data that contains both normal and abnormal data.The normal data indicates the normal behavior of the grid whereas the abnormal data indicates fault or abnormal conditions in power grid.As a result,detecting anomalies/abnormal conditions in the fast flowing PMU data that reflects the status of the power system is critical.A novel methodology for detecting and categorizing abnormalities in streaming PMU data is presented in this paper.The proposed method consists of three modules namely,offline Gaussian Mixture Model(GMM),online GMM for identifying anomalies and clustering ensemble model for classifying the anomalies.The significant features of the proposed method are detecting anomalies while taking into account of multivariate nature of the PMU dataset,adapting to concept drift in the flowing PMU data without retraining the existing model unnecessarily and classifying the anomalies.The proposed model is implemented in Python and the testing results prove that the proposed model is well suited for detection and classification of anomalies on the fly.
基金the National Key Research and Development Program(No.2018YFB1307005)the Smart Medical Project of Shanghai Municipal Commission of Health and Family Planning(No.2018ZHYL0226)。
文摘The objective of this study is to construct a multi-department symptom-based automatic diagnosis model.However,it is dificult to establish a model to classify plenty of diseases and collect thousands of disease-symptom datasets simultaneously.Inspired by the thought of"knowledge graph is model",this study proposes to build an experience-infused knowledge model by continuously learning the experiential knowledge from data,and incrementally injecting it into the knowledge graph.Therefore,incremental learning and injection are used to solve the data collection problem,and the knowledge graph is modeled and containerized to solve the large-scale multi-classification problems.First,an entity linking method is designed and a heterogeneous knowledge graph is constructed by graph fusion.Then,an adaptive neural network model is constructed for each dataset,and the data is used for statistical initialization and model training.Finally,the weights and biases of the learned neural network model are updated to the knowledge graph.It is worth noting that for the incremental process,we consider both the data and class increments.We evaluate the diagnostic effectiveness of the model on the current dataset and the anti-forgetting ability on the historical dataset after class increment on three public datasets.Compared with the classical model,the proposed model improves the diagnostic accuracy of the three datasets by 5%,2%,and 15%on average,respectively.Meanwhile,the model under incremental learning has a better ability to resist forgetting.
基金This work was funded by the National Natural Science Foundation of China Nos.U22A2099,61966009,62006057the Graduate Innovation Program No.YCSW2022286.
文摘Humans are experiencing the inclusion of artificial agents in their lives,such as unmanned vehicles,service robots,voice assistants,and intelligent medical care.If the artificial agents cannot align with social values or make ethical decisions,they may not meet the expectations of humans.Traditionally,an ethical decision-making framework is constructed by rule-based or statistical approaches.In this paper,we propose an ethical decision-making framework based on incremental ILP(Inductive Logic Programming),which can overcome the brittleness of rule-based approaches and little interpretability of statistical approaches.As the current incremental ILP makes it difficult to solve conflicts,we propose a novel ethical decision-making framework considering conflicts in this paper,which adopts our proposed incremental ILP system.The framework consists of two processes:the learning process and the deduction process.The first process records bottom clauses with their score functions and learns rules guided by the entailment and the score function.The second process obtains an ethical decision based on the rules.In an ethical scenario about chatbots for teenagers’mental health,we verify that our framework can learn ethical rules and make ethical decisions.Besides,we extract incremental ILP from the framework and compare it with the state-of-the-art ILP systems based on ASP(Answer Set Programming)focusing on conflict resolution.The results of comparisons show that our proposed system can generate better-quality rules than most other systems.
基金financially supported by the National Natural Science Foundation of China (Grant Nos. 52074258, 41941018, and U21A20153)
文摘Based on data from the Jilin Water Diversion Tunnels from the Songhua River(China),an improved and real-time prediction method optimized by multi-algorithm for tunnel boring machine(TBM)cutter-head torque is presented.Firstly,a function excluding invalid and abnormal data is established to distinguish TBM operating state,and a feature selection method based on the SelectKBest algorithm is proposed.Accordingly,ten features that are most closely related to the cutter-head torque are selected as input variables,which,in descending order of influence,include the sum of motor torque,cutter-head power,sum of motor power,sum of motor current,advance rate,cutter-head pressure,total thrust force,penetration rate,cutter-head rotational velocity,and field penetration index.Secondly,a real-time cutterhead torque prediction model’s structure is developed,based on the bidirectional long short-term memory(BLSTM)network integrating the dropout algorithm to prevent overfitting.Then,an algorithm to optimize hyperparameters of model based on Bayesian and cross-validation is proposed.Early stopping and checkpoint algorithms are integrated to optimize the training process.Finally,a BLSTMbased real-time cutter-head torque prediction model is developed,which fully utilizes the previous time-series tunneling information.The mean absolute percentage error(MAPE)of the model in the verification section is 7.3%,implying that the presented model is suitable for real-time cutter-head torque prediction.Furthermore,an incremental learning method based on the above base model is introduced to improve the adaptability of the model during the TBM tunneling.Comparison of the prediction performance between the base and incremental learning models in the same tunneling section shows that:(1)the MAPE of the predicted results of the BLSTM-based real-time cutter-head torque prediction model remains below 10%,and both the coefficient of determination(R^(2))and correlation coefficient(r)between measured and predicted values exceed 0.95;and(2)the incremental learning method is suitable for realtime cutter-head torque prediction and can effectively improve the prediction accuracy and generalization capacity of the model during the excavation process.
基金the National Natural Science Foundation of China(No.50775219)
文摘In view of the incremental learning problem of manifold learning algorithm, an adaptive neighborhood incremental principal component analysis(PCA) and locality preserving projection(LPP) manifold learning algorithm is presented, and the incremental learning principle of algorithm is introduced. For incremental sample data, the adjacency and covariance matrices are incrementally updated by the existing samples; then the dimensionality reduction results of the incremental samples are estimated by the dimensionality reduction results of the existing samples; finally, the dimensionality reduction results of the incremental and existing samples are updated by subspace iteration method. The adaptive neighborhood incremental PCA-LPP manifold learning algorithm is applied to processing of gearbox fault signals. The dimensionality reduction results by incremental learning have very small error, compared with those by batch learning. Spatial aggregation of the incremental samples is basically stable, and fault identification rate is increased.
基金supported by the National Natural Science Foundation of China under Grant Nos.62002187,62002189,61972383,61972237 and 61976127the Science Research Project of Hebei Education Department of China under Grant No.QN2023184。
文摘Wearable health monitoring is a crucial technical tool that offers early warning for chronic diseases due to its superior portability and low power consumption.However,most wearable health data is distributed across dfferent organizations,such as hospitals,research institutes,and companies,and can only be accessed by the owners of the data in compliance with data privacy regulations.The first challenge addressed in this paper is communicating in a privacy-preserving manner among different organizations.The second technical challenge is handling the dynamic expansion of the federation without model retraining.To address the first challenge,we propose a horizontal federated learning method called Federated Extremely Random Forest(FedERF).Its contribution-based splitting score computing mechanism significantly mitigates the impact of privacy protection constraints on model performance.Based on FedERF,we present a federated incremental learning method called Federated Incremental Extremely Random Forest(FedIERF)to address the second technical challenge.FedIERF introduces a hardness-driven weighting mechanism and an importance-based updating scheme to update the existing federated model incrementally.The experiments show that FedERF achieves comparable performance with non-federated methods,and FedIERF effectively addresses the dynamic expansion of the federation.This opens up opportunities for cooperation between different organizations in wearable health monitoring.
