For current physical examination and medical institutions, the majority of Medical projects need a long time to get the result, this wastes user too much time. In order to solve this problem, this paper presents a pro...For current physical examination and medical institutions, the majority of Medical projects need a long time to get the result, this wastes user too much time. In order to solve this problem, this paper presents a program to achieve viewing the results of examination anytime or anywhere with the help of cell phone applications software. The program proposed to solve the problem that user have to wait for the medical test results for a long time and cause the time waste, compared to the programs which using e-mail, SMS and mailing report card to inform the user the results, this one is to be more efficient, while also better meet user and medical institutions’ requirements. In addition, in order to test the practicality and feasibility of the program, with IOS devices as the material basis, this paper constructs a Web client system, based on the GPRS technology, we can realize the data communications between the Web server and a client which is the workbench IOS devices. Through testing, we can proof this Web client system has certain practical and development value, with the physical examination institutions become popular gradually, I believe that this design ideas of the system will be used extensively in the medical system, and also will bring a person more convenient.展开更多
With the increasing complexity of vehicular networks and the proliferation of connected vehicles,Federated Learning(FL)has emerged as a critical framework for decentralized model training while preserving data privacy...With the increasing complexity of vehicular networks and the proliferation of connected vehicles,Federated Learning(FL)has emerged as a critical framework for decentralized model training while preserving data privacy.However,efficient client selection and adaptive weight allocation in heterogeneous and non-IID environments remain challenging.To address these issues,we propose Federated Learning with Client Selection and Adaptive Weighting(FedCW),a novel algorithm that leverages adaptive client selection and dynamic weight allocation for optimizing model convergence in real-time vehicular networks.FedCW selects clients based on their Euclidean distance from the global model and dynamically adjusts aggregation weights to optimize both data diversity and model convergence.Experimental results show that FedCW significantly outperforms existing FL algorithms such as FedAvg,FedProx,and SCAFFOLD,particularly in non-IID settings,achieving faster convergence,higher accuracy,and reduced communication overhead.These findings demonstrate that FedCW provides an effective solution for enhancing the performance of FL in heterogeneous,edge-based computing environments.展开更多
This paper is focused on the technique for de si gn and realization of the process communications about the computer-aided train diagram network system. The Windows Socket technique is adopted to program for the cli...This paper is focused on the technique for de si gn and realization of the process communications about the computer-aided train diagram network system. The Windows Socket technique is adopted to program for the client and the server to create system applications and solve the problems o f data transfer and data sharing in the system.展开更多
In this paper, we present a client-server system for 3D scene change detection. A 3D scene point cloud which stored on the server is reconstructed by (structure-from-motion) SfM technique in advance. On the other hand...In this paper, we present a client-server system for 3D scene change detection. A 3D scene point cloud which stored on the server is reconstructed by (structure-from-motion) SfM technique in advance. On the other hand, the client system in tablets captures query images and sent them to the server to estimate the change area. In order to find region of change, an existing change detection method has been applied into our system. Then the server sends detection result image back to mobile device and visualize it. The result of system test shows that the system could detect change cor- rectly.展开更多
Build a general software development platform for industrial process supervisor and management system by combining the technology of industrial configuration and Client/Server model, and introduce the architecture and...Build a general software development platform for industrial process supervisor and management system by combining the technology of industrial configuration and Client/Server model, and introduce the architecture and topological application of this platform. It puts forward a solution to the real time problem in the industrial distributed supervisor system.展开更多
The client server mode conforms to the trend of the development of MIS. Based on a project of the Guangzhou Power Supply Bureau, the research method and optimization strategy of MIS are put forward using the client ...The client server mode conforms to the trend of the development of MIS. Based on a project of the Guangzhou Power Supply Bureau, the research method and optimization strategy of MIS are put forward using the client server computation mode in a power system enterprise. The structure and function are also introduced.展开更多
The proliferation of deep learning(DL)has amplified the demand for processing large and complex datasets for tasks such as modeling,classification,and identification.However,traditional DL methods compromise client pr...The proliferation of deep learning(DL)has amplified the demand for processing large and complex datasets for tasks such as modeling,classification,and identification.However,traditional DL methods compromise client privacy by collecting sensitive data,underscoring the necessity for privacy-preserving solutions like Federated Learning(FL).FL effectively addresses escalating privacy concerns by facilitating collaborative model training without necessitating the sharing of raw data.Given that FL clients autonomously manage training data,encouraging client engagement is pivotal for successful model training.To overcome challenges like unreliable communication and budget constraints,we present ENTIRE,a contract-based dynamic participation incentive mechanism for FL.ENTIRE ensures impartial model training by tailoring participation levels and payments to accommodate diverse client preferences.Our approach involves several key steps.Initially,we examine how random client participation impacts FL convergence in non-convex scenarios,establishing the correlation between client participation levels and model performance.Subsequently,we reframe model performance optimization as an optimal contract design challenge to guide the distribution of rewards among clients with varying participation costs.By balancing budget considerations with model effectiveness,we craft optimal contracts for different budgetary constraints,prompting clients to disclose their participation preferences and select suitable contracts for contributing to model training.Finally,we conduct a comprehensive experimental evaluation of ENTIRE using three real datasets.The results demonstrate a significant 12.9%enhancement in model performance,validating its adherence to anticipated economic properties.展开更多
Large-scale neural networks-based federated learning(FL)has gained public recognition for its effective capabilities in distributed training.Nonetheless,the open system architecture inherent to federated learning syst...Large-scale neural networks-based federated learning(FL)has gained public recognition for its effective capabilities in distributed training.Nonetheless,the open system architecture inherent to federated learning systems raises concerns regarding their vulnerability to potential attacks.Poisoning attacks turn into a major menace to federated learning on account of their concealed property and potent destructive force.By altering the local model during routine machine learning training,attackers can easily contaminate the global model.Traditional detection and aggregation solutions mitigate certain threats,but they are still insufficient to completely eliminate the influence generated by attackers.Therefore,federated unlearning that can remove unreliable models while maintaining the accuracy of the global model has become a solution.Unfortunately some existing federated unlearning approaches are rather difficult to be applied in large neural network models because of their high computational expenses.Hence,we propose SlideFU,an efficient anti-poisoning attack federated unlearning framework.The primary concept of SlideFU is to employ sliding window to construct the training process,where all operations are confined within the window.We design a malicious detection scheme based on principal component analysis(PCA),which calculates the trust factors between compressed models in a low-cost way to eliminate unreliable models.After confirming that the global model is under attack,the system activates the federated unlearning process,calibrates the gradients based on the updated direction of the calibration gradients.Experiments on two public datasets demonstrate that our scheme can recover a robust model with extremely high efficiency.展开更多
The growing demand for privacy-preserving machine learning has positioned federated learning as a promising research paradigm,enabling the training of high-performance models across distributed data sources without co...The growing demand for privacy-preserving machine learning has positioned federated learning as a promising research paradigm,enabling the training of high-performance models across distributed data sources without compromising user privacy.However,despite its advantages,federated learning faces critical challenges arising from the heterogeneity and volatility of participating clients.In real-world scenarios,variations in client participation,data volume,computational capability,and communication reliability contribute to a highly dynamic training environment,which negatively impacts efficiency and convergence of the model.To address these challenges,this paper proposes a novel client selection method named CDE3.First,CDE3 employs a multidimensional model to comprehensively evaluate clients’contributions.Second,we enhance the classical Exp3 algorithm by incorporating a discount factor that exponentially decays historical contributions,thereby increasing the influence of recent client behavior in the selection process.Furthermore,we provide a theoretical analysis demonstrating a favorable regret bound for the proposed method.Extensive experiments conducted in volatile FL settings validate the effectiveness of CDE3,showing improved convergence speed and model accuracy compared with those of the baseline algorithms.These results confirm that CDE3 effectively mitigates volatility,enhancing the stability and efficiency of federated learning.展开更多
文摘For current physical examination and medical institutions, the majority of Medical projects need a long time to get the result, this wastes user too much time. In order to solve this problem, this paper presents a program to achieve viewing the results of examination anytime or anywhere with the help of cell phone applications software. The program proposed to solve the problem that user have to wait for the medical test results for a long time and cause the time waste, compared to the programs which using e-mail, SMS and mailing report card to inform the user the results, this one is to be more efficient, while also better meet user and medical institutions’ requirements. In addition, in order to test the practicality and feasibility of the program, with IOS devices as the material basis, this paper constructs a Web client system, based on the GPRS technology, we can realize the data communications between the Web server and a client which is the workbench IOS devices. Through testing, we can proof this Web client system has certain practical and development value, with the physical examination institutions become popular gradually, I believe that this design ideas of the system will be used extensively in the medical system, and also will bring a person more convenient.
文摘With the increasing complexity of vehicular networks and the proliferation of connected vehicles,Federated Learning(FL)has emerged as a critical framework for decentralized model training while preserving data privacy.However,efficient client selection and adaptive weight allocation in heterogeneous and non-IID environments remain challenging.To address these issues,we propose Federated Learning with Client Selection and Adaptive Weighting(FedCW),a novel algorithm that leverages adaptive client selection and dynamic weight allocation for optimizing model convergence in real-time vehicular networks.FedCW selects clients based on their Euclidean distance from the global model and dynamically adjusts aggregation weights to optimize both data diversity and model convergence.Experimental results show that FedCW significantly outperforms existing FL algorithms such as FedAvg,FedProx,and SCAFFOLD,particularly in non-IID settings,achieving faster convergence,higher accuracy,and reduced communication overhead.These findings demonstrate that FedCW provides an effective solution for enhancing the performance of FL in heterogeneous,edge-based computing environments.
文摘This paper is focused on the technique for de si gn and realization of the process communications about the computer-aided train diagram network system. The Windows Socket technique is adopted to program for the client and the server to create system applications and solve the problems o f data transfer and data sharing in the system.
文摘In this paper, we present a client-server system for 3D scene change detection. A 3D scene point cloud which stored on the server is reconstructed by (structure-from-motion) SfM technique in advance. On the other hand, the client system in tablets captures query images and sent them to the server to estimate the change area. In order to find region of change, an existing change detection method has been applied into our system. Then the server sends detection result image back to mobile device and visualize it. The result of system test shows that the system could detect change cor- rectly.
基金The Natural Science Foundation of Hunan Province!(No.96 10 1 3 0 )
文摘Build a general software development platform for industrial process supervisor and management system by combining the technology of industrial configuration and Client/Server model, and introduce the architecture and topological application of this platform. It puts forward a solution to the real time problem in the industrial distributed supervisor system.
文摘The client server mode conforms to the trend of the development of MIS. Based on a project of the Guangzhou Power Supply Bureau, the research method and optimization strategy of MIS are put forward using the client server computation mode in a power system enterprise. The structure and function are also introduced.
基金supported by the National Natural Science Foundation of China(Nos.62072411,62372343,62402352,62403500)the Key Research and Development Program of Hubei Province(No.2023BEB024)the Open Fund of Key Laboratory of Social Computing and Cognitive Intelligence(Dalian University of Technology),Ministry of Education(No.SCCI2024TB02).
文摘The proliferation of deep learning(DL)has amplified the demand for processing large and complex datasets for tasks such as modeling,classification,and identification.However,traditional DL methods compromise client privacy by collecting sensitive data,underscoring the necessity for privacy-preserving solutions like Federated Learning(FL).FL effectively addresses escalating privacy concerns by facilitating collaborative model training without necessitating the sharing of raw data.Given that FL clients autonomously manage training data,encouraging client engagement is pivotal for successful model training.To overcome challenges like unreliable communication and budget constraints,we present ENTIRE,a contract-based dynamic participation incentive mechanism for FL.ENTIRE ensures impartial model training by tailoring participation levels and payments to accommodate diverse client preferences.Our approach involves several key steps.Initially,we examine how random client participation impacts FL convergence in non-convex scenarios,establishing the correlation between client participation levels and model performance.Subsequently,we reframe model performance optimization as an optimal contract design challenge to guide the distribution of rewards among clients with varying participation costs.By balancing budget considerations with model effectiveness,we craft optimal contracts for different budgetary constraints,prompting clients to disclose their participation preferences and select suitable contracts for contributing to model training.Finally,we conduct a comprehensive experimental evaluation of ENTIRE using three real datasets.The results demonstrate a significant 12.9%enhancement in model performance,validating its adherence to anticipated economic properties.
基金supported in part by the National Social Science Foundation of China under Grant 20BTQ058in part by the Natural Science Foundation of Hunan Province under Grant 2023JJ50033.
文摘Large-scale neural networks-based federated learning(FL)has gained public recognition for its effective capabilities in distributed training.Nonetheless,the open system architecture inherent to federated learning systems raises concerns regarding their vulnerability to potential attacks.Poisoning attacks turn into a major menace to federated learning on account of their concealed property and potent destructive force.By altering the local model during routine machine learning training,attackers can easily contaminate the global model.Traditional detection and aggregation solutions mitigate certain threats,but they are still insufficient to completely eliminate the influence generated by attackers.Therefore,federated unlearning that can remove unreliable models while maintaining the accuracy of the global model has become a solution.Unfortunately some existing federated unlearning approaches are rather difficult to be applied in large neural network models because of their high computational expenses.Hence,we propose SlideFU,an efficient anti-poisoning attack federated unlearning framework.The primary concept of SlideFU is to employ sliding window to construct the training process,where all operations are confined within the window.We design a malicious detection scheme based on principal component analysis(PCA),which calculates the trust factors between compressed models in a low-cost way to eliminate unreliable models.After confirming that the global model is under attack,the system activates the federated unlearning process,calibrates the gradients based on the updated direction of the calibration gradients.Experiments on two public datasets demonstrate that our scheme can recover a robust model with extremely high efficiency.
基金funded by the Central University of Finance and Economics,Greater Bay Area Research Institute Project(No.YJY202303)the National Natural Science Foundation of China(No.61906220)the Ministry of Education of Humanities and Social Science Project(No.19YJCZH178).
文摘The growing demand for privacy-preserving machine learning has positioned federated learning as a promising research paradigm,enabling the training of high-performance models across distributed data sources without compromising user privacy.However,despite its advantages,federated learning faces critical challenges arising from the heterogeneity and volatility of participating clients.In real-world scenarios,variations in client participation,data volume,computational capability,and communication reliability contribute to a highly dynamic training environment,which negatively impacts efficiency and convergence of the model.To address these challenges,this paper proposes a novel client selection method named CDE3.First,CDE3 employs a multidimensional model to comprehensively evaluate clients’contributions.Second,we enhance the classical Exp3 algorithm by incorporating a discount factor that exponentially decays historical contributions,thereby increasing the influence of recent client behavior in the selection process.Furthermore,we provide a theoretical analysis demonstrating a favorable regret bound for the proposed method.Extensive experiments conducted in volatile FL settings validate the effectiveness of CDE3,showing improved convergence speed and model accuracy compared with those of the baseline algorithms.These results confirm that CDE3 effectively mitigates volatility,enhancing the stability and efficiency of federated learning.
