Dear Editor,Te Veterans Health Administration(VHA)provides healthcare for over 9 million enrolled veterans with approximately 2.7 million of those residing in rural areas[1].Te MISSION Act of 2018 emphasizes VHA colla...Dear Editor,Te Veterans Health Administration(VHA)provides healthcare for over 9 million enrolled veterans with approximately 2.7 million of those residing in rural areas[1].Te MISSION Act of 2018 emphasizes VHA collaboration with Federally Qualifed Healthcare Centers(FQHC)to serve rural residing veterans and nearly all existing collaborations involve arrangement of payment for community-based care by VHA to FQHCs.Unfortunately,there is a paucity of descriptive clinical data on existing cross-system collaborations which may help characterize these veterans and aid understanding of conditions for which they may receive treatment across systems.Such data has implications for workforce training,development,and resource allocation[2].Te objective of this report is to describe diferent clinical profles between two mutually exclusive samples:veterans engaged in FQHC only use,and VHA-enrolled veterans engaged in dual VHA and FQHC use.展开更多
In recent years,the type and quantity of news are growing rapidly,and it is not easy for users to find the news they are interested in the massive amount of news.A news recommendation system can score and predict the ...In recent years,the type and quantity of news are growing rapidly,and it is not easy for users to find the news they are interested in the massive amount of news.A news recommendation system can score and predict the candidate news,and finally recommend the news with high scores to users.However,existing user models usually only consider users’long-term interests and ignore users’recent interests,which affects users’usage experience.Therefore,this paper introduces gated recurrent unit(GRU)sequence network to capture users’short-term interests and combines users’short-term interests and long-terminterests to characterize users.While existing models often only use the user’s browsing history and ignore the variability of different users’interest in the same news,we introduce additional user’s ID information and apply the personalized attention mechanism for user representation.Thus,we achieve a more accurate user representation.We also consider the risk of compromising user privacy if the user model training is placed on the server side.To solve this problem,we design the training of the user model locally on the client side by introducing a federated learning framework to keep the user’s browsing history on the client side.We further employ secure multiparty computation to request news representations from the server side,which protects privacy to some extent.Extensive experiments on a real-world news dataset show that our proposed news recommendation model has a better improvement in several performance evaluation metrics.Compared with the current state-of-the-art federated news recommendation models,our model has increased by 0.54%in AUC,1.97%in MRR,2.59%in nDCG@5%,and 1.89%in nDCG@10.At the same time,because we use a federated learning framework,compared with other centralized news recommendation methods,we achieve privacy protection for users.展开更多
Load forecasting is a crucial aspect of intelligent Virtual Power Plant(VPP)management and ameans of balancing the relationship between distributed power grids and traditional power grids.However,due to the continuous...Load forecasting is a crucial aspect of intelligent Virtual Power Plant(VPP)management and ameans of balancing the relationship between distributed power grids and traditional power grids.However,due to the continuous emergence of power consumption peaks,the power supply quality of the power grid cannot be guaranteed.Therefore,an intelligent calculation method is required to effectively predict the load,enabling better power grid dispatching and ensuring the stable operation of the power grid.This paper proposes a decentralized heterogeneous federated distillation learning algorithm(DHFDL)to promote trusted federated learning(FL)between different federates in the blockchain.The algorithm comprises two stages:common knowledge accumulation and personalized training.In the first stage,each federate on the blockchain is treated as ameta-distribution.After aggregating the knowledge of each federate circularly,the model is uploaded to the blockchain.In the second stage,other federates on the blockchain download the trained model for personalized training,both of which are based on knowledge distillation.Experimental results demonstrate that the DHFDL algorithmproposed in this paper can resist a higher proportion of malicious code compared to FedAvg and a Blockchain-based Federated Learning framework with Committee consensus(BFLC).Additionally,by combining asynchronous consensus with the FL model training process,the DHFDL training time is the shortest,and the training efficiency of decentralized FL is improved.展开更多
In this work is developed a proposal of environment indicators needed for the Environment Impact Assessment (EIA) process in Mexico’s Federal District (FD);through which are authorized the construction and realizatio...In this work is developed a proposal of environment indicators needed for the Environment Impact Assessment (EIA) process in Mexico’s Federal District (FD);through which are authorized the construction and realization of different work actions and activities. The methodology is based on the combination of cabinet and field work, performed in three stages. In the first, a documental review was carried out within the topic of Environment Impact (EI), the EIA and the study area, with a subsequent analysis of the environment indicators at an international, national and regional scale. In the second, the systematization of information was performed for the sixteen study cases at a local scale and the organization and analysis of a data base with the allotted information. And in the last stage, a field work was realized with participative observations in three verification sites and interview applications to the principal actors of the EIA process. These results allowed: to determine the main limitations within the EIA process (methodological, technical and operational), to propose an indicators scheme, and to formulate recommendations focused on the improvement of this Environment Public Policy instrument.展开更多
This study analyzes the demarcation method of riverine and accreted land of the Brazilian Federal Heritage Department and proposes the incorporation of the flow rate corresponding to the recurrence interval of two yea...This study analyzes the demarcation method of riverine and accreted land of the Brazilian Federal Heritage Department and proposes the incorporation of the flow rate corresponding to the recurrence interval of two years, as recommended by the State Environmental Institute of the state of Rio de Janeiro. The case study of the Rio de Janeiro section of the Paraiba do Sul River was investigated, and the results indicate that the Federal Heritage Department’s method does not consider the ongoing anthropization of the river, caused mainly by the construction and operation of hydroelectric plants. In addition, it was observed that the limnimetric scales of the studied gauging stations are influenced by constant changes in the riverbed and by riverbank occupation, making it difficult to estimate the ordinary flood level. The study concludes by suggesting the adoption of a flow rate with a recurrence interval of two years and the simulation of the runoff conditions for demarcation of the average ordinary flood line.展开更多
In 2018, US President Donald Trump repeatedly and publicly criticized the US Federal Reserve for raising interest rates too quickly, breaking the long-established precedent for presidents to refrain from intervening i...In 2018, US President Donald Trump repeatedly and publicly criticized the US Federal Reserve for raising interest rates too quickly, breaking the long-established precedent for presidents to refrain from intervening in monetary policy and putting the independence of the Federal Reserve into question. However, this is only the latest development of a longer process: since the financial crisis, the Federal Reserve has been gradually losing its independence, in a quiet and perhaps permanent way. There are several reasons for this trend: the Federal Reserve’s performance during the financial crisis undermined its credibility, the consolidation of political factors arranged against its independence, and the consequences of the financial crisis weakened the economic foundation for its independence. Trump’s rise to power has only strengthened these factors, bringing an additional loss of independence, which will have a profound impact on the economy, society, and politics.展开更多
Introduction: Dyspareunia is one of the most common complaints in gynae-cologic practice with tremendous effect on both quality of life and sexual rela-tionship of women. Objectives: To determine the prevalence of dys...Introduction: Dyspareunia is one of the most common complaints in gynae-cologic practice with tremendous effect on both quality of life and sexual rela-tionship of women. Objectives: To determine the prevalence of dyspareunia and its effect on sexual life among gynaecology clinic attendees in Alex Ekwueme Federal University Teaching Hospital, Abakaliki. Materials and Methods: A cross-sectional study was conducted on consenting participants between 12th May 2016 and 25th July 2016. Anonymous self-administered questionnaires were used collection information on dyspareunia and its effect on sexual life at the Gynaecology clinic. The data was analyzed using Epiinfo version 7.1.5. Results: One hundred and four (104) women participated in this study. Most of the women studied were Igbos (95.19%), and were mainly between the age ranges of 21 - 30 years (66.35%). Most of them were married (89.42%), and were also mainly of the Pentecostal denomination (40.78%). The mean age at coitarche was 20.6 ± 3.95 years. Prevalence of dyspareunia was 36% and only 16% sought medical help. The various responses to dyspareunia were avoidance of sex 11%, reduced frequency of intercourse 8%, less desire for sex 19%, while majority of women with dyspareunia tolerated it (62%). Conclusion: The prevalence of dyspareunia is high in our society afflicting young women in their reproductive years with associated enormous stress on their sexual life.展开更多
DEAR SIRS,Informenergo-Scientific and Technological Information Centerin Power Engineering and Electrification of Federal Russia is a stateorganization which deals with:-inquire and information servicing of enterprise...DEAR SIRS,Informenergo-Scientific and Technological Information Centerin Power Engineering and Electrification of Federal Russia is a stateorganization which deals with:-inquire and information servicing of enterprises,organizations,specialists with different scientific and technological data in thefield of power engineering (allotment of copies of original docu-ments,data bases,access to data bases,information about展开更多
FEDERAL MOGUL
在汽车零部件行业,每当人们提起Champion(冠军)、Ferodo(菲罗多)、Goetze(戈茨)、Payen(培英)、AE等品牌时,无不为其悠久的历史、精良的品质和享誉全球的知名度而倾倒,这些国际品牌在如林的竞争对手中独树一帜、久负盛誉...FEDERAL MOGUL
在汽车零部件行业,每当人们提起Champion(冠军)、Ferodo(菲罗多)、Goetze(戈茨)、Payen(培英)、AE等品牌时,无不为其悠久的历史、精良的品质和享誉全球的知名度而倾倒,这些国际品牌在如林的竞争对手中独树一帜、久负盛誉.它们犹如一颗颗璀灿的明星,汇集成了一个耀眼的星座,它就是当今世界汽车零部件工业的巨子、集二十余个世界著名品牌于一身的跨国集团--Federal-Mogul(辉门)公司.展开更多
Plain Language has made a great difference nowadays. As it turns out, Plain Language works effectively to express clearly, concisely and systematically. However, it is necessary for contemporary practitioners to revie...Plain Language has made a great difference nowadays. As it turns out, Plain Language works effectively to express clearly, concisely and systematically. However, it is necessary for contemporary practitioners to review the origin and development of Plain Language Movement and to examine whether it has thoroughly implemented Plain Language policies in every federal document. Examining a contemporary federal document against the Guidelines for Document Designers reveals existing problems for further development.展开更多
Federated learning(FL)development has grown increasingly strong with the increased emphasis on data for individuals and industry.Federated learning allows individual participants to jointly train a global model withou...Federated learning(FL)development has grown increasingly strong with the increased emphasis on data for individuals and industry.Federated learning allows individual participants to jointly train a global model without sharing local data,which significantly enhances data privacy.However,federated learning is vulnerable to poisoning attacks by malicious participants.Since federated learning does not have access to the participants’training process,i.e.,attackers can compromise the global model by uploading elaborate malicious local updates to the server under the guise of normal participants.Current model poisoning attacks usually add small perturbations to the local model after it is trained to craft harmful local updates and the attacker finds the appropriate perturbation size to bypass robust detection methods and corrupt the global model as much as possible.In contrast,we propose a novel model poisoning attack based on the momentum of history information(MPHM),that is,the attacker makes new malicious updates by dynamically crafting perturbations using the historical information in the local training,which will make the new malicious updates more effective and stealthy.Our attack aims to indiscriminately reduce the testing accuracy of the global model with minimal information.Experiments show that in the classical defense case,our attack can significantly corrupt the accuracy of the global model compared to other advanced poisoning attacks.展开更多
This study examines the relationship between job satisfaction and performance,investigating personality traits and satisfaction aspects among employees of a Federal Higher Education Institution.A questionnaire was adm...This study examines the relationship between job satisfaction and performance,investigating personality traits and satisfaction aspects among employees of a Federal Higher Education Institution.A questionnaire was administered to 658 participants,using structural equation modeling for analysis.Results highlighted that challenging work,neuroticism,and self-esteem significantly influenced overall workplace satisfaction,while general satisfaction,self-efficacy,and lack of attention were key determinants of work performance.This emphasizes the importance for managers to prioritize factors enhancing employee satisfaction,as it positively correlates with job performance.展开更多
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
With the development of hardware devices and the upgrading of smartphones,a large number of users save privacy-related information in mobile devices,mainly smartphones,which puts forward higher demands on the protecti...With the development of hardware devices and the upgrading of smartphones,a large number of users save privacy-related information in mobile devices,mainly smartphones,which puts forward higher demands on the protection of mobile users’privacy information.At present,mobile user authenticationmethods based on humancomputer interaction have been extensively studied due to their advantages of high precision and non-perception,but there are still shortcomings such as low data collection efficiency,untrustworthy participating nodes,and lack of practicability.To this end,this paper proposes a privacy-enhanced mobile user authentication method with motion sensors,which mainly includes:(1)Construct a smart contract-based private chain and federated learning to improve the data collection efficiency of mobile user authentication,reduce the probability of the model being bypassed by attackers,and reduce the overhead of data centralized processing and the risk of privacy leakage;(2)Use certificateless encryption to realize the authentication of the device to ensure the credibility of the client nodes participating in the calculation;(3)Combine Variational Mode Decomposition(VMD)and Long Short-TermMemory(LSTM)to analyze and model the motion sensor data of mobile devices to improve the accuracy of model certification.The experimental results on the real environment dataset of 1513 people show that themethod proposed in this paper can effectively resist poisoning attacks while ensuring the accuracy and efficiency of mobile user authentication.展开更多
This paper underscores a critique of Nigeria's choice of a federal option,the current state of which has led to the incessant clamour,across the nation for restructuring.Federalism in Nigeria since 1960 was adopte...This paper underscores a critique of Nigeria's choice of a federal option,the current state of which has led to the incessant clamour,across the nation for restructuring.Federalism in Nigeria since 1960 was adopted to accommodate the nation^heterogeneous culture with the sole aim of maintaining unity in diversity.The dysfunctional system has been observed to be the main bane of Nigeria underdevelopment,instability,and absence of good governance all of which can negatively affect the achievement of a sustainable national development plan like the Vision 2030.This paper argues that Nigeria is yet to evolve a truly federal system capable of taking care of its numerous challenges.Writing from a historical perspective,the paper uncovers that there are logical inconsistencies in Nigeria’s practice of federalism.What gets here is a hidden unitary framework.The call for restructuring is in a general sense borne out of some apparent degrees of foul play and disparity presently perceived by the part units emerging from defective federalism.Understood in the above is that the necessary ingredients of sustainable democracy and governance are completely lacking in Nigeria.Thus,if the proposed Vision 2030 or any other national development plan is to be accomplished,there has to be a restructuring of the dysfunctional political system;a restructuring that devolves power to the federating units leaving the federal government with vital aspects like defence,foreign affairs among others;a return to the regional arrangement of the past.展开更多
Explainable Artificial Intelligence(XAI)has an advanced feature to enhance the decision-making feature and improve the rule-based technique by using more advanced Machine Learning(ML)and Deep Learning(DL)based algorit...Explainable Artificial Intelligence(XAI)has an advanced feature to enhance the decision-making feature and improve the rule-based technique by using more advanced Machine Learning(ML)and Deep Learning(DL)based algorithms.In this paper,we chose e-healthcare systems for efficient decision-making and data classification,especially in data security,data handling,diagnostics,laboratories,and decision-making.Federated Machine Learning(FML)is a new and advanced technology that helps to maintain privacy for Personal Health Records(PHR)and handle a large amount of medical data effectively.In this context,XAI,along with FML,increases efficiency and improves the security of e-healthcare systems.The experiments show efficient system performance by implementing a federated averaging algorithm on an open-source Federated Learning(FL)platform.The experimental evaluation demonstrates the accuracy rate by taking epochs size 5,batch size 16,and the number of clients 5,which shows a higher accuracy rate(19,104).We conclude the paper by discussing the existing gaps and future work in an e-healthcare system.展开更多
As a representative emerging machine learning technique, federated learning(FL) has gained considerable popularity for its special feature of “making data available but not visible”. However, potential problems rema...As a representative emerging machine learning technique, federated learning(FL) has gained considerable popularity for its special feature of “making data available but not visible”. However, potential problems remain, including privacy breaches, imbalances in payment, and inequitable distribution.These shortcomings let devices reluctantly contribute relevant data to, or even refuse to participate in FL. Therefore, in the application of FL, an important but also challenging issue is to motivate as many participants as possible to provide high-quality data to FL. In this paper, we propose an incentive mechanism for FL based on the continuous zero-determinant(CZD) strategies from the perspective of game theory. We first model the interaction between the server and the devices during the FL process as a continuous iterative game. We then apply the CZD strategies for two players and then multiple players to optimize the social welfare of FL, for which we prove that the server can keep social welfare at a high and stable level. Subsequently, we design an incentive mechanism based on the CZD strategies to attract devices to contribute all of their high-accuracy data to FL.Finally, we perform simulations to demonstrate that our proposed CZD-based incentive mechanism can indeed generate high and stable social welfare in FL.展开更多
When data privacy is imposed as a necessity,Federated learning(FL)emerges as a relevant artificial intelligence field for developing machine learning(ML)models in a distributed and decentralized environment.FL allows ...When data privacy is imposed as a necessity,Federated learning(FL)emerges as a relevant artificial intelligence field for developing machine learning(ML)models in a distributed and decentralized environment.FL allows ML models to be trained on local devices without any need for centralized data transfer,thereby reducing both the exposure of sensitive data and the possibility of data interception by malicious third parties.This paradigm has gained momentum in the last few years,spurred by the plethora of real-world applications that have leveraged its ability to improve the efficiency of distributed learning and to accommodate numerous participants with their data sources.By virtue of FL,models can be learned from all such distributed data sources while preserving data privacy.The aim of this paper is to provide a practical tutorial on FL,including a short methodology and a systematic analysis of existing software frameworks.Furthermore,our tutorial provides exemplary cases of study from three complementary perspectives:i)Foundations of FL,describing the main components of FL,from key elements to FL categories;ii)Implementation guidelines and exemplary cases of study,by systematically examining the functionalities provided by existing software frameworks for FL deployment,devising a methodology to design a FL scenario,and providing exemplary cases of study with source code for different ML approaches;and iii)Trends,shortly reviewing a non-exhaustive list of research directions that are under active investigation in the current FL landscape.The ultimate purpose of this work is to establish itself as a referential work for researchers,developers,and data scientists willing to explore the capabilities of FL in practical applications.展开更多
Scalability and information personal privacy are vital for training and deploying large-scale deep learning models.Federated learning trains models on exclusive information by aggregating weights from various devices ...Scalability and information personal privacy are vital for training and deploying large-scale deep learning models.Federated learning trains models on exclusive information by aggregating weights from various devices and taking advantage of the device-agnostic environment of web browsers.Nevertheless,relying on a main central server for internet browser-based federated systems can prohibit scalability and interfere with the training process as a result of growing client numbers.Additionally,information relating to the training dataset can possibly be extracted from the distributed weights,potentially reducing the privacy of the local data used for training.In this research paper,we aim to investigate the challenges of scalability and data privacy to increase the efficiency of distributed training models.As a result,we propose a web-federated learning exchange(WebFLex)framework,which intends to improve the decentralization of the federated learning process.WebFLex is additionally developed to secure distributed and scalable federated learning systems that operate in web browsers across heterogeneous devices.Furthermore,WebFLex utilizes peer-to-peer interactions and secure weight exchanges utilizing browser-to-browser web real-time communication(WebRTC),efficiently preventing the need for a main central server.WebFLex has actually been measured in various setups using the MNIST dataset.Experimental results show WebFLex’s ability to improve the scalability of federated learning systems,allowing a smooth increase in the number of participating devices without central data aggregation.In addition,WebFLex can maintain a durable federated learning procedure even when faced with device disconnections and network variability.Additionally,it improves data privacy by utilizing artificial noise,which accomplishes an appropriate balance between accuracy and privacy preservation.展开更多
As a distributed machine learning method,federated learning(FL)has the advantage of naturally protecting data privacy.It keeps data locally and trains local models through local data to protect the privacy of local da...As a distributed machine learning method,federated learning(FL)has the advantage of naturally protecting data privacy.It keeps data locally and trains local models through local data to protect the privacy of local data.The federated learning method effectively solves the problem of artificial Smart data islands and privacy protection issues.However,existing research shows that attackersmay still steal user information by analyzing the parameters in the federated learning training process and the aggregation parameters on the server side.To solve this problem,differential privacy(DP)techniques are widely used for privacy protection in federated learning.However,adding Gaussian noise perturbations to the data degrades the model learning performance.To address these issues,this paper proposes a differential privacy federated learning scheme based on adaptive Gaussian noise(DPFL-AGN).To protect the data privacy and security of the federated learning training process,adaptive Gaussian noise is specifically added in the training process to hide the real parameters uploaded by the client.In addition,this paper proposes an adaptive noise reduction method.With the convergence of the model,the Gaussian noise in the later stage of the federated learning training process is reduced adaptively.This paper conducts a series of simulation experiments on realMNIST and CIFAR-10 datasets,and the results show that the DPFL-AGN algorithmperforms better compared to the other algorithms.展开更多
基金supported in part by an award from the VHA Office of Rural Health,Veterans Rural Health Resource CenterDIowa City(VRHRC-IC),Iowa City VA Health Care System,Iowa City,IA(Award#7345)。
文摘Dear Editor,Te Veterans Health Administration(VHA)provides healthcare for over 9 million enrolled veterans with approximately 2.7 million of those residing in rural areas[1].Te MISSION Act of 2018 emphasizes VHA collaboration with Federally Qualifed Healthcare Centers(FQHC)to serve rural residing veterans and nearly all existing collaborations involve arrangement of payment for community-based care by VHA to FQHCs.Unfortunately,there is a paucity of descriptive clinical data on existing cross-system collaborations which may help characterize these veterans and aid understanding of conditions for which they may receive treatment across systems.Such data has implications for workforce training,development,and resource allocation[2].Te objective of this report is to describe diferent clinical profles between two mutually exclusive samples:veterans engaged in FQHC only use,and VHA-enrolled veterans engaged in dual VHA and FQHC use.
文摘In recent years,the type and quantity of news are growing rapidly,and it is not easy for users to find the news they are interested in the massive amount of news.A news recommendation system can score and predict the candidate news,and finally recommend the news with high scores to users.However,existing user models usually only consider users’long-term interests and ignore users’recent interests,which affects users’usage experience.Therefore,this paper introduces gated recurrent unit(GRU)sequence network to capture users’short-term interests and combines users’short-term interests and long-terminterests to characterize users.While existing models often only use the user’s browsing history and ignore the variability of different users’interest in the same news,we introduce additional user’s ID information and apply the personalized attention mechanism for user representation.Thus,we achieve a more accurate user representation.We also consider the risk of compromising user privacy if the user model training is placed on the server side.To solve this problem,we design the training of the user model locally on the client side by introducing a federated learning framework to keep the user’s browsing history on the client side.We further employ secure multiparty computation to request news representations from the server side,which protects privacy to some extent.Extensive experiments on a real-world news dataset show that our proposed news recommendation model has a better improvement in several performance evaluation metrics.Compared with the current state-of-the-art federated news recommendation models,our model has increased by 0.54%in AUC,1.97%in MRR,2.59%in nDCG@5%,and 1.89%in nDCG@10.At the same time,because we use a federated learning framework,compared with other centralized news recommendation methods,we achieve privacy protection for users.
基金supported by the Research and application of Power Business Data Security and Trusted Collaborative Sharing Technology Based on Blockchain and Multi-Party Security Computing(J2022057).
文摘Load forecasting is a crucial aspect of intelligent Virtual Power Plant(VPP)management and ameans of balancing the relationship between distributed power grids and traditional power grids.However,due to the continuous emergence of power consumption peaks,the power supply quality of the power grid cannot be guaranteed.Therefore,an intelligent calculation method is required to effectively predict the load,enabling better power grid dispatching and ensuring the stable operation of the power grid.This paper proposes a decentralized heterogeneous federated distillation learning algorithm(DHFDL)to promote trusted federated learning(FL)between different federates in the blockchain.The algorithm comprises two stages:common knowledge accumulation and personalized training.In the first stage,each federate on the blockchain is treated as ameta-distribution.After aggregating the knowledge of each federate circularly,the model is uploaded to the blockchain.In the second stage,other federates on the blockchain download the trained model for personalized training,both of which are based on knowledge distillation.Experimental results demonstrate that the DHFDL algorithmproposed in this paper can resist a higher proportion of malicious code compared to FedAvg and a Blockchain-based Federated Learning framework with Committee consensus(BFLC).Additionally,by combining asynchronous consensus with the FL model training process,the DHFDL training time is the shortest,and the training efficiency of decentralized FL is improved.
文摘In this work is developed a proposal of environment indicators needed for the Environment Impact Assessment (EIA) process in Mexico’s Federal District (FD);through which are authorized the construction and realization of different work actions and activities. The methodology is based on the combination of cabinet and field work, performed in three stages. In the first, a documental review was carried out within the topic of Environment Impact (EI), the EIA and the study area, with a subsequent analysis of the environment indicators at an international, national and regional scale. In the second, the systematization of information was performed for the sixteen study cases at a local scale and the organization and analysis of a data base with the allotted information. And in the last stage, a field work was realized with participative observations in three verification sites and interview applications to the principal actors of the EIA process. These results allowed: to determine the main limitations within the EIA process (methodological, technical and operational), to propose an indicators scheme, and to formulate recommendations focused on the improvement of this Environment Public Policy instrument.
文摘This study analyzes the demarcation method of riverine and accreted land of the Brazilian Federal Heritage Department and proposes the incorporation of the flow rate corresponding to the recurrence interval of two years, as recommended by the State Environmental Institute of the state of Rio de Janeiro. The case study of the Rio de Janeiro section of the Paraiba do Sul River was investigated, and the results indicate that the Federal Heritage Department’s method does not consider the ongoing anthropization of the river, caused mainly by the construction and operation of hydroelectric plants. In addition, it was observed that the limnimetric scales of the studied gauging stations are influenced by constant changes in the riverbed and by riverbank occupation, making it difficult to estimate the ordinary flood level. The study concludes by suggesting the adoption of a flow rate with a recurrence interval of two years and the simulation of the runoff conditions for demarcation of the average ordinary flood line.
文摘In 2018, US President Donald Trump repeatedly and publicly criticized the US Federal Reserve for raising interest rates too quickly, breaking the long-established precedent for presidents to refrain from intervening in monetary policy and putting the independence of the Federal Reserve into question. However, this is only the latest development of a longer process: since the financial crisis, the Federal Reserve has been gradually losing its independence, in a quiet and perhaps permanent way. There are several reasons for this trend: the Federal Reserve’s performance during the financial crisis undermined its credibility, the consolidation of political factors arranged against its independence, and the consequences of the financial crisis weakened the economic foundation for its independence. Trump’s rise to power has only strengthened these factors, bringing an additional loss of independence, which will have a profound impact on the economy, society, and politics.
文摘Introduction: Dyspareunia is one of the most common complaints in gynae-cologic practice with tremendous effect on both quality of life and sexual rela-tionship of women. Objectives: To determine the prevalence of dyspareunia and its effect on sexual life among gynaecology clinic attendees in Alex Ekwueme Federal University Teaching Hospital, Abakaliki. Materials and Methods: A cross-sectional study was conducted on consenting participants between 12th May 2016 and 25th July 2016. Anonymous self-administered questionnaires were used collection information on dyspareunia and its effect on sexual life at the Gynaecology clinic. The data was analyzed using Epiinfo version 7.1.5. Results: One hundred and four (104) women participated in this study. Most of the women studied were Igbos (95.19%), and were mainly between the age ranges of 21 - 30 years (66.35%). Most of them were married (89.42%), and were also mainly of the Pentecostal denomination (40.78%). The mean age at coitarche was 20.6 ± 3.95 years. Prevalence of dyspareunia was 36% and only 16% sought medical help. The various responses to dyspareunia were avoidance of sex 11%, reduced frequency of intercourse 8%, less desire for sex 19%, while majority of women with dyspareunia tolerated it (62%). Conclusion: The prevalence of dyspareunia is high in our society afflicting young women in their reproductive years with associated enormous stress on their sexual life.
文摘DEAR SIRS,Informenergo-Scientific and Technological Information Centerin Power Engineering and Electrification of Federal Russia is a stateorganization which deals with:-inquire and information servicing of enterprises,organizations,specialists with different scientific and technological data in thefield of power engineering (allotment of copies of original docu-ments,data bases,access to data bases,information about
文摘Plain Language has made a great difference nowadays. As it turns out, Plain Language works effectively to express clearly, concisely and systematically. However, it is necessary for contemporary practitioners to review the origin and development of Plain Language Movement and to examine whether it has thoroughly implemented Plain Language policies in every federal document. Examining a contemporary federal document against the Guidelines for Document Designers reveals existing problems for further development.
基金supported in part by the National Key R&D Program of China(2020YFB1712401,2018YFB1701400)the Nature Science Foundation of China(62006210,62001284,62206252)+7 种基金the Key Scientific and Technology Project of Henan Province of China(221100210100)the Key Project of Public Benefit in Henan Province of China(201300210500)the Research Foundation for Advanced Talents of Zhengzhou University(32340306)the Key Research Projects of Universities in Henan Province of China(7A520015,21B520018)Fundamental Science Projects of Railway Police College(2020TJJBKY002)Advanced research project of SongShan Laboratory(YYJC022022001)The Key R&D and Promotion Project in Science and Technology of Henan(232102210154)the Key Scientific and Technological Research Projects in Henan Province of China(192102310216).
文摘Federated learning(FL)development has grown increasingly strong with the increased emphasis on data for individuals and industry.Federated learning allows individual participants to jointly train a global model without sharing local data,which significantly enhances data privacy.However,federated learning is vulnerable to poisoning attacks by malicious participants.Since federated learning does not have access to the participants’training process,i.e.,attackers can compromise the global model by uploading elaborate malicious local updates to the server under the guise of normal participants.Current model poisoning attacks usually add small perturbations to the local model after it is trained to craft harmful local updates and the attacker finds the appropriate perturbation size to bypass robust detection methods and corrupt the global model as much as possible.In contrast,we propose a novel model poisoning attack based on the momentum of history information(MPHM),that is,the attacker makes new malicious updates by dynamically crafting perturbations using the historical information in the local training,which will make the new malicious updates more effective and stealthy.Our attack aims to indiscriminately reduce the testing accuracy of the global model with minimal information.Experiments show that in the classical defense case,our attack can significantly corrupt the accuracy of the global model compared to other advanced poisoning attacks.
文摘This study examines the relationship between job satisfaction and performance,investigating personality traits and satisfaction aspects among employees of a Federal Higher Education Institution.A questionnaire was administered to 658 participants,using structural equation modeling for analysis.Results highlighted that challenging work,neuroticism,and self-esteem significantly influenced overall workplace satisfaction,while general satisfaction,self-efficacy,and lack of attention were key determinants of work performance.This emphasizes the importance for managers to prioritize factors enhancing employee satisfaction,as it positively correlates with job performance.
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.
基金Wenzhou Key Scientific and Technological Projects(No.ZG2020031)Wenzhou Polytechnic Research Projects(No.WZY2021002)+3 种基金Key R&D Projects in Zhejiang Province(No.2021C01117)Major Program of Natural Science Foundation of Zhejiang Province(LD22F020002)the Cloud Security Key Technology Research Laboratorythe Researchers Supporting Project Number(RSP2023R509),King Saud University,Riyadh,Saudi Arabia.
文摘With the development of hardware devices and the upgrading of smartphones,a large number of users save privacy-related information in mobile devices,mainly smartphones,which puts forward higher demands on the protection of mobile users’privacy information.At present,mobile user authenticationmethods based on humancomputer interaction have been extensively studied due to their advantages of high precision and non-perception,but there are still shortcomings such as low data collection efficiency,untrustworthy participating nodes,and lack of practicability.To this end,this paper proposes a privacy-enhanced mobile user authentication method with motion sensors,which mainly includes:(1)Construct a smart contract-based private chain and federated learning to improve the data collection efficiency of mobile user authentication,reduce the probability of the model being bypassed by attackers,and reduce the overhead of data centralized processing and the risk of privacy leakage;(2)Use certificateless encryption to realize the authentication of the device to ensure the credibility of the client nodes participating in the calculation;(3)Combine Variational Mode Decomposition(VMD)and Long Short-TermMemory(LSTM)to analyze and model the motion sensor data of mobile devices to improve the accuracy of model certification.The experimental results on the real environment dataset of 1513 people show that themethod proposed in this paper can effectively resist poisoning attacks while ensuring the accuracy and efficiency of mobile user authentication.
文摘This paper underscores a critique of Nigeria's choice of a federal option,the current state of which has led to the incessant clamour,across the nation for restructuring.Federalism in Nigeria since 1960 was adopted to accommodate the nation^heterogeneous culture with the sole aim of maintaining unity in diversity.The dysfunctional system has been observed to be the main bane of Nigeria underdevelopment,instability,and absence of good governance all of which can negatively affect the achievement of a sustainable national development plan like the Vision 2030.This paper argues that Nigeria is yet to evolve a truly federal system capable of taking care of its numerous challenges.Writing from a historical perspective,the paper uncovers that there are logical inconsistencies in Nigeria’s practice of federalism.What gets here is a hidden unitary framework.The call for restructuring is in a general sense borne out of some apparent degrees of foul play and disparity presently perceived by the part units emerging from defective federalism.Understood in the above is that the necessary ingredients of sustainable democracy and governance are completely lacking in Nigeria.Thus,if the proposed Vision 2030 or any other national development plan is to be accomplished,there has to be a restructuring of the dysfunctional political system;a restructuring that devolves power to the federating units leaving the federal government with vital aspects like defence,foreign affairs among others;a return to the regional arrangement of the past.
文摘Explainable Artificial Intelligence(XAI)has an advanced feature to enhance the decision-making feature and improve the rule-based technique by using more advanced Machine Learning(ML)and Deep Learning(DL)based algorithms.In this paper,we chose e-healthcare systems for efficient decision-making and data classification,especially in data security,data handling,diagnostics,laboratories,and decision-making.Federated Machine Learning(FML)is a new and advanced technology that helps to maintain privacy for Personal Health Records(PHR)and handle a large amount of medical data effectively.In this context,XAI,along with FML,increases efficiency and improves the security of e-healthcare systems.The experiments show efficient system performance by implementing a federated averaging algorithm on an open-source Federated Learning(FL)platform.The experimental evaluation demonstrates the accuracy rate by taking epochs size 5,batch size 16,and the number of clients 5,which shows a higher accuracy rate(19,104).We conclude the paper by discussing the existing gaps and future work in an e-healthcare system.
基金partially supported by the National Natural Science Foundation of China (62173308)the Natural Science Foundation of Zhejiang Province of China (LR20F030001)the Jinhua Science and Technology Project (2022-1-042)。
文摘As a representative emerging machine learning technique, federated learning(FL) has gained considerable popularity for its special feature of “making data available but not visible”. However, potential problems remain, including privacy breaches, imbalances in payment, and inequitable distribution.These shortcomings let devices reluctantly contribute relevant data to, or even refuse to participate in FL. Therefore, in the application of FL, an important but also challenging issue is to motivate as many participants as possible to provide high-quality data to FL. In this paper, we propose an incentive mechanism for FL based on the continuous zero-determinant(CZD) strategies from the perspective of game theory. We first model the interaction between the server and the devices during the FL process as a continuous iterative game. We then apply the CZD strategies for two players and then multiple players to optimize the social welfare of FL, for which we prove that the server can keep social welfare at a high and stable level. Subsequently, we design an incentive mechanism based on the CZD strategies to attract devices to contribute all of their high-accuracy data to FL.Finally, we perform simulations to demonstrate that our proposed CZD-based incentive mechanism can indeed generate high and stable social welfare in FL.
基金the R&D&I,Spain grants PID2020-119478GB-I00 and,PID2020-115832GB-I00 funded by MCIN/AEI/10.13039/501100011033.N.Rodríguez-Barroso was supported by the grant FPU18/04475 funded by MCIN/AEI/10.13039/501100011033 and by“ESF Investing in your future”Spain.J.Moyano was supported by a postdoctoral Juan de la Cierva Formación grant FJC2020-043823-I funded by MCIN/AEI/10.13039/501100011033 and by European Union NextGenerationEU/PRTR.J.Del Ser acknowledges funding support from the Spanish Centro para el Desarrollo Tecnológico Industrial(CDTI)through the AI4ES projectthe Department of Education of the Basque Government(consolidated research group MATHMODE,IT1456-22)。
文摘When data privacy is imposed as a necessity,Federated learning(FL)emerges as a relevant artificial intelligence field for developing machine learning(ML)models in a distributed and decentralized environment.FL allows ML models to be trained on local devices without any need for centralized data transfer,thereby reducing both the exposure of sensitive data and the possibility of data interception by malicious third parties.This paradigm has gained momentum in the last few years,spurred by the plethora of real-world applications that have leveraged its ability to improve the efficiency of distributed learning and to accommodate numerous participants with their data sources.By virtue of FL,models can be learned from all such distributed data sources while preserving data privacy.The aim of this paper is to provide a practical tutorial on FL,including a short methodology and a systematic analysis of existing software frameworks.Furthermore,our tutorial provides exemplary cases of study from three complementary perspectives:i)Foundations of FL,describing the main components of FL,from key elements to FL categories;ii)Implementation guidelines and exemplary cases of study,by systematically examining the functionalities provided by existing software frameworks for FL deployment,devising a methodology to design a FL scenario,and providing exemplary cases of study with source code for different ML approaches;and iii)Trends,shortly reviewing a non-exhaustive list of research directions that are under active investigation in the current FL landscape.The ultimate purpose of this work is to establish itself as a referential work for researchers,developers,and data scientists willing to explore the capabilities of FL in practical applications.
基金This work has been funded by King Saud University,Riyadh,Saudi Arabia,through Researchers Supporting Project Number(RSPD2024R857).
文摘Scalability and information personal privacy are vital for training and deploying large-scale deep learning models.Federated learning trains models on exclusive information by aggregating weights from various devices and taking advantage of the device-agnostic environment of web browsers.Nevertheless,relying on a main central server for internet browser-based federated systems can prohibit scalability and interfere with the training process as a result of growing client numbers.Additionally,information relating to the training dataset can possibly be extracted from the distributed weights,potentially reducing the privacy of the local data used for training.In this research paper,we aim to investigate the challenges of scalability and data privacy to increase the efficiency of distributed training models.As a result,we propose a web-federated learning exchange(WebFLex)framework,which intends to improve the decentralization of the federated learning process.WebFLex is additionally developed to secure distributed and scalable federated learning systems that operate in web browsers across heterogeneous devices.Furthermore,WebFLex utilizes peer-to-peer interactions and secure weight exchanges utilizing browser-to-browser web real-time communication(WebRTC),efficiently preventing the need for a main central server.WebFLex has actually been measured in various setups using the MNIST dataset.Experimental results show WebFLex’s ability to improve the scalability of federated learning systems,allowing a smooth increase in the number of participating devices without central data aggregation.In addition,WebFLex can maintain a durable federated learning procedure even when faced with device disconnections and network variability.Additionally,it improves data privacy by utilizing artificial noise,which accomplishes an appropriate balance between accuracy and privacy preservation.
基金the Sichuan Provincial Science and Technology Department Project under Grant 2019YFN0104the Yibin Science and Technology Plan Project under Grant 2021GY008the Sichuan University of Science and Engineering Postgraduate Innovation Fund Project under Grant Y2022154.
文摘As a distributed machine learning method,federated learning(FL)has the advantage of naturally protecting data privacy.It keeps data locally and trains local models through local data to protect the privacy of local data.The federated learning method effectively solves the problem of artificial Smart data islands and privacy protection issues.However,existing research shows that attackersmay still steal user information by analyzing the parameters in the federated learning training process and the aggregation parameters on the server side.To solve this problem,differential privacy(DP)techniques are widely used for privacy protection in federated learning.However,adding Gaussian noise perturbations to the data degrades the model learning performance.To address these issues,this paper proposes a differential privacy federated learning scheme based on adaptive Gaussian noise(DPFL-AGN).To protect the data privacy and security of the federated learning training process,adaptive Gaussian noise is specifically added in the training process to hide the real parameters uploaded by the client.In addition,this paper proposes an adaptive noise reduction method.With the convergence of the model,the Gaussian noise in the later stage of the federated learning training process is reduced adaptively.This paper conducts a series of simulation experiments on realMNIST and CIFAR-10 datasets,and the results show that the DPFL-AGN algorithmperforms better compared to the other algorithms.
