典型文献
Challenges and future directions of secure federated learning:a survey
文献摘要：
Federated learning came into being with the increasing concern of privacy security,as people's sensitive information is being exposed under the era of big data.It is an algorithm that does not collect users'raw data,but aggregates model parameters from each client and therefore protects user's privacy.Nonetheless,due to the inherent distributed nature of federated learning,it is more vulnerable under attacks since users may upload malicious data to break down the federated learning server.In addition,some recent studies have shown that attackers can recover information merely from parameters.Hence,there is still lots of room to improve the current federated learning frameworks.In this survey,we give a brief review of the state-of-the-art federated learning techniques and detailedly discuss the improvement of federated learning.Several open issues and existing solutions in federated learning are discussed.We also point out the future research directions of federated learning.
文献关键词：
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作者姓名：
Kaiyue ZHANG;Xuan SONG;Chenhan ZHANG;Shui YU
作者机构：
Department of Computer Science and Engineering,Southern University of Science and Technology,Shenzhen 518055,China;Faculty of Engineering and Information Technology,University of Technology Sydney,Sydney 2007,Australia;SUSTech-UTokyo Joint Research Center on Super Smart City,Department of Computer Science and Engineering,Southern University of Science and Technology,Shenzhen 518055,China;Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation,Department of Computer Science and Engineering,Southern University of Science and Technology,Shenzhen 518055,China
文献出处：
引用格式：
[1]Kaiyue ZHANG;Xuan SONG;Chenhan ZHANG;Shui YU-.Challenges and future directions of secure federated learning:a survey)[J].计算机科学前沿,2022(05):164-171
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Challenges,future,directions,secure,federated,learning,survey,Federated,came,into,being,increasing,concern,privacy,security,people,sensitive,information,exposed,under,big,data,It,algorithm,that,does,not,collect,users,raw,aggregates,model,parameters,from,each,client,therefore,protects,Nonetheless,due,inherent,distributed,nature,more,vulnerable,attacks,since,may,upload,malicious,break,down,server,In,addition,some,recent,studies,have,shown,attackers,can,recover,merely,Hence,still,lots,room,current,frameworks,this,we,give,brief,review,state,art,techniques,detailedly,improvement,Several,open,issues,existing,solutions,are,discussed,We,also,point,out,research
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0.647576
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