Data sharing and privacy protection are made possible by federated learning,which allows for continuous model parameter sharing between several clients and a central server.Multiple reliable and high-quality clients m...Data sharing and privacy protection are made possible by federated learning,which allows for continuous model parameter sharing between several clients and a central server.Multiple reliable and high-quality clients must participate in practical applications for the federated learning global model to be accurate,but because the clients are independent,the central server cannot fully control their behavior.The central server has no way of knowing the correctness of the model parameters provided by each client in this round,so clients may purposefully or unwittingly submit anomalous data,leading to abnormal behavior,such as becoming malicious attackers or defective clients.To reduce their negative consequences,it is crucial to quickly detect these abnormalities and incentivize them.In this paper,we propose a Federated Learning framework for Detecting and Incentivizing Abnormal Clients(FL-DIAC)to accomplish efficient and security federated learning.We build a detector that introduces an auto-encoder for anomaly detection and use it to perform anomaly identification and prevent the involvement of abnormal clients,in particular for the anomaly client detection problem.Among them,before the model parameters are input to the detector,we propose a Fourier transform-based anomaly data detectionmethod for dimensionality reduction in order to reduce the computational complexity.Additionally,we create a credit scorebased incentive structure to encourage clients to participate in training in order tomake clients actively participate.Three training models(CNN,MLP,and ResNet-18)and three datasets(MNIST,Fashion MNIST,and CIFAR-10)have been used in experiments.According to theoretical analysis and experimental findings,the FL-DIAC is superior to other federated learning schemes of the same type in terms of effectiveness.展开更多
Federated learning enables data owners in the Internet of Things(IoT)to collaborate in training models without sharing private data,creating new business opportunities for building a data market.However,in practical o...Federated learning enables data owners in the Internet of Things(IoT)to collaborate in training models without sharing private data,creating new business opportunities for building a data market.However,in practical operation,there are still some problems with federated learning applications.Blockchain has the characteristics of decentralization,distribution,and security.The blockchain-enabled federated learning further improve the security and performance of model training,while also expanding the application scope of federated learning.Blockchain has natural financial attributes that help establish a federated learning data market.However,the data of federated learning tasks may be distributed across a large number of resource-constrained IoT devices,which have different computing,communication,and storage resources,and the data quality of each device may also vary.Therefore,how to effectively select the clients with the data required for federated learning task is a research hotspot.In this paper,a two-stage client selection scheme for blockchain-enabled federated learning is proposed,which first selects clients that satisfy federated learning task through attribute-based encryption,protecting the attribute privacy of clients.Then blockchain nodes select some clients for local model aggregation by proximal policy optimization algorithm.Experiments show that the model performance of our two-stage client selection scheme is higher than that of other client selection algorithms when some clients are offline and the data quality is poor.展开更多
Today, in the field of computer networks, new services have been developed on the Internet or intranets, including the mail server, database management, sounds, videos and the web server itself Apache. The number of s...Today, in the field of computer networks, new services have been developed on the Internet or intranets, including the mail server, database management, sounds, videos and the web server itself Apache. The number of solutions for this server is therefore growing continuously, these services are becoming more and more complex and expensive, without being able to fulfill the needs of the users. The absence of benchmarks for websites with dynamic content is the major obstacle to research in this area. These users place high demands on the speed of access to information on the Internet. This is why the performance of the web server is critically important. Several factors influence performance, such as server execution speed, network saturation on the internet or intranet, increased response time, and throughputs. By measuring these factors, we propose a performance evaluation strategy for servers that allows us to determine the actual performance of different servers in terms of user satisfaction. Furthermore, we identified performance characteristics such as throughput, resource utilization, and response time of a system through measurement and modeling by simulation. Finally, we present a simple queue model of an Apache web server, which reasonably represents the behavior of a saturated web server using the Simulink model in Matlab (Matrix Laboratory) and also incorporates sporadic incoming traffic. We obtain server performance metrics such as average response time and throughput through simulations. Compared to other models, our model is conceptually straightforward. The model has been validated through measurements and simulations during the tests that we conducted.展开更多
The rapid expansion of artificial intelligence(AI)applications has raised significant concerns about user privacy,prompting the development of privacy-preserving machine learning(ML)paradigms such as federated learnin...The rapid expansion of artificial intelligence(AI)applications has raised significant concerns about user privacy,prompting the development of privacy-preserving machine learning(ML)paradigms such as federated learning(FL).FL enables the distributed training of ML models,keeping data on local devices and thus addressing the privacy concerns of users.However,challenges arise from the heterogeneous nature of mobile client devices,partial engagement of training,and non-independent identically distributed(non-IID)data distribution,leading to performance degradation and optimization objective bias in FL training.With the development of 5G/6G networks and the integration of cloud computing edge computing resources,globally distributed cloud computing resources can be effectively utilized to optimize the FL process.Through the specific parameters of the server through the selection mechanism,it does not increase the monetary cost and reduces the network latency overhead,but also balances the objectives of communication optimization and low engagement mitigation that cannot be achieved simultaneously in a single-server framework of existing works.In this paper,we propose the FedAdaSS algorithm,an adaptive parameter server selection mechanism designed to optimize the training efficiency in each round of FL training by selecting the most appropriate server as the parameter server.Our approach leverages the flexibility of cloud resource computing power,and allows organizers to strategically select servers for data broadcasting and aggregation,thus improving training performance while maintaining cost efficiency.The FedAdaSS algorithm estimates the utility of client systems and servers and incorporates an adaptive random reshuffling strategy that selects the optimal server in each round of the training process.Theoretical analysis confirms the convergence of FedAdaSS under strong convexity and L-smooth assumptions,and comparative experiments within the FLSim framework demonstrate a reduction in training round-to-accuracy by 12%–20%compared to the Federated Averaging(FedAvg)with random reshuffling method under unique server.Furthermore,FedAdaSS effectively mitigates performance loss caused by low client engagement,reducing the loss indicator by 50%.展开更多
This study developed a mail server program using Socket API and Python.The program uses the Hypertext Transfer Protocol(HTTP)to receive emails from browser clients and forward them to actual email service providers vi...This study developed a mail server program using Socket API and Python.The program uses the Hypertext Transfer Protocol(HTTP)to receive emails from browser clients and forward them to actual email service providers via the Simple Mail Transfer Protocol(SMTP).As a web server,it handles Transmission Control Protocol(TCP)connection requests from browsers,receives HTTP commands and email data,and temporarily stores the emails in a file.Simultaneously,as an SMTP client,the program establishes a TCP connection with the actual mail server,sends SMTP commands,and transmits the previously saved emails.In addition,we also analyzed security issues and the efficiency and availability of this server,providing insights into the design of SMTP mail servers.展开更多
基金supported by Key Research and Development Program of China (No.2022YFC3005401)Key Research and Development Program of Yunnan Province,China (Nos.202203AA080009,202202AF080003)+1 种基金Science and Technology Achievement Transformation Program of Jiangsu Province,China (BA2021002)Fundamental Research Funds for the Central Universities (Nos.B220203006,B210203024).
文摘Data sharing and privacy protection are made possible by federated learning,which allows for continuous model parameter sharing between several clients and a central server.Multiple reliable and high-quality clients must participate in practical applications for the federated learning global model to be accurate,but because the clients are independent,the central server cannot fully control their behavior.The central server has no way of knowing the correctness of the model parameters provided by each client in this round,so clients may purposefully or unwittingly submit anomalous data,leading to abnormal behavior,such as becoming malicious attackers or defective clients.To reduce their negative consequences,it is crucial to quickly detect these abnormalities and incentivize them.In this paper,we propose a Federated Learning framework for Detecting and Incentivizing Abnormal Clients(FL-DIAC)to accomplish efficient and security federated learning.We build a detector that introduces an auto-encoder for anomaly detection and use it to perform anomaly identification and prevent the involvement of abnormal clients,in particular for the anomaly client detection problem.Among them,before the model parameters are input to the detector,we propose a Fourier transform-based anomaly data detectionmethod for dimensionality reduction in order to reduce the computational complexity.Additionally,we create a credit scorebased incentive structure to encourage clients to participate in training in order tomake clients actively participate.Three training models(CNN,MLP,and ResNet-18)and three datasets(MNIST,Fashion MNIST,and CIFAR-10)have been used in experiments.According to theoretical analysis and experimental findings,the FL-DIAC is superior to other federated learning schemes of the same type in terms of effectiveness.
文摘Federated learning enables data owners in the Internet of Things(IoT)to collaborate in training models without sharing private data,creating new business opportunities for building a data market.However,in practical operation,there are still some problems with federated learning applications.Blockchain has the characteristics of decentralization,distribution,and security.The blockchain-enabled federated learning further improve the security and performance of model training,while also expanding the application scope of federated learning.Blockchain has natural financial attributes that help establish a federated learning data market.However,the data of federated learning tasks may be distributed across a large number of resource-constrained IoT devices,which have different computing,communication,and storage resources,and the data quality of each device may also vary.Therefore,how to effectively select the clients with the data required for federated learning task is a research hotspot.In this paper,a two-stage client selection scheme for blockchain-enabled federated learning is proposed,which first selects clients that satisfy federated learning task through attribute-based encryption,protecting the attribute privacy of clients.Then blockchain nodes select some clients for local model aggregation by proximal policy optimization algorithm.Experiments show that the model performance of our two-stage client selection scheme is higher than that of other client selection algorithms when some clients are offline and the data quality is poor.
文摘Today, in the field of computer networks, new services have been developed on the Internet or intranets, including the mail server, database management, sounds, videos and the web server itself Apache. The number of solutions for this server is therefore growing continuously, these services are becoming more and more complex and expensive, without being able to fulfill the needs of the users. The absence of benchmarks for websites with dynamic content is the major obstacle to research in this area. These users place high demands on the speed of access to information on the Internet. This is why the performance of the web server is critically important. Several factors influence performance, such as server execution speed, network saturation on the internet or intranet, increased response time, and throughputs. By measuring these factors, we propose a performance evaluation strategy for servers that allows us to determine the actual performance of different servers in terms of user satisfaction. Furthermore, we identified performance characteristics such as throughput, resource utilization, and response time of a system through measurement and modeling by simulation. Finally, we present a simple queue model of an Apache web server, which reasonably represents the behavior of a saturated web server using the Simulink model in Matlab (Matrix Laboratory) and also incorporates sporadic incoming traffic. We obtain server performance metrics such as average response time and throughput through simulations. Compared to other models, our model is conceptually straightforward. The model has been validated through measurements and simulations during the tests that we conducted.
基金supported in part by the National Natural Science Foundation of China under Grant U22B2005,Grant 62372462.
文摘The rapid expansion of artificial intelligence(AI)applications has raised significant concerns about user privacy,prompting the development of privacy-preserving machine learning(ML)paradigms such as federated learning(FL).FL enables the distributed training of ML models,keeping data on local devices and thus addressing the privacy concerns of users.However,challenges arise from the heterogeneous nature of mobile client devices,partial engagement of training,and non-independent identically distributed(non-IID)data distribution,leading to performance degradation and optimization objective bias in FL training.With the development of 5G/6G networks and the integration of cloud computing edge computing resources,globally distributed cloud computing resources can be effectively utilized to optimize the FL process.Through the specific parameters of the server through the selection mechanism,it does not increase the monetary cost and reduces the network latency overhead,but also balances the objectives of communication optimization and low engagement mitigation that cannot be achieved simultaneously in a single-server framework of existing works.In this paper,we propose the FedAdaSS algorithm,an adaptive parameter server selection mechanism designed to optimize the training efficiency in each round of FL training by selecting the most appropriate server as the parameter server.Our approach leverages the flexibility of cloud resource computing power,and allows organizers to strategically select servers for data broadcasting and aggregation,thus improving training performance while maintaining cost efficiency.The FedAdaSS algorithm estimates the utility of client systems and servers and incorporates an adaptive random reshuffling strategy that selects the optimal server in each round of the training process.Theoretical analysis confirms the convergence of FedAdaSS under strong convexity and L-smooth assumptions,and comparative experiments within the FLSim framework demonstrate a reduction in training round-to-accuracy by 12%–20%compared to the Federated Averaging(FedAvg)with random reshuffling method under unique server.Furthermore,FedAdaSS effectively mitigates performance loss caused by low client engagement,reducing the loss indicator by 50%.
文摘This study developed a mail server program using Socket API and Python.The program uses the Hypertext Transfer Protocol(HTTP)to receive emails from browser clients and forward them to actual email service providers via the Simple Mail Transfer Protocol(SMTP).As a web server,it handles Transmission Control Protocol(TCP)connection requests from browsers,receives HTTP commands and email data,and temporarily stores the emails in a file.Simultaneously,as an SMTP client,the program establishes a TCP connection with the actual mail server,sends SMTP commands,and transmits the previously saved emails.In addition,we also analyzed security issues and the efficiency and availability of this server,providing insights into the design of SMTP mail servers.
