{"title":"Quality Inference in Federated Learning With Secure Aggregation","authors":"Balázs Pejó;Gergely Biczók","doi":"10.1109/TBDATA.2023.3280406","DOIUrl":null,"url":null,"abstract":"Federated learning algorithms are developed both for efficiency reasons and to ensure the privacy and confidentiality of personal and business data, respectively. Despite no data being shared explicitly, recent studies showed that the mechanism could still leak sensitive information. Hence, secure aggregation is utilized in many real-world scenarios to prevent attribution to specific participants. In this paper, we focus on the quality (i.e., the ratio of correct labels) of individual training datasets and show that such quality information could be inferred and attributed to specific participants even when secure aggregation is applied. Specifically, through a series of image recognition experiments, we infer the relative quality ordering of participants. Moreover, we apply the inferred quality information to stabilize training performance, measure the individual contribution of participants, and detect misbehavior.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"9 5","pages":"1430-1437"},"PeriodicalIF":7.5000,"publicationDate":"2023-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/6687317/10236926/10138056.pdf","citationCount":"12","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Big Data","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10138056/","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 12
Abstract
Federated learning algorithms are developed both for efficiency reasons and to ensure the privacy and confidentiality of personal and business data, respectively. Despite no data being shared explicitly, recent studies showed that the mechanism could still leak sensitive information. Hence, secure aggregation is utilized in many real-world scenarios to prevent attribution to specific participants. In this paper, we focus on the quality (i.e., the ratio of correct labels) of individual training datasets and show that such quality information could be inferred and attributed to specific participants even when secure aggregation is applied. Specifically, through a series of image recognition experiments, we infer the relative quality ordering of participants. Moreover, we apply the inferred quality information to stabilize training performance, measure the individual contribution of participants, and detect misbehavior.
期刊介绍:
The IEEE Transactions on Big Data publishes peer-reviewed articles focusing on big data. These articles present innovative research ideas and application results across disciplines, including novel theories, algorithms, and applications. Research areas cover a wide range, such as big data analytics, visualization, curation, management, semantics, infrastructure, standards, performance analysis, intelligence extraction, scientific discovery, security, privacy, and legal issues specific to big data. The journal also prioritizes applications of big data in fields generating massive datasets.