安全聚合联合学习中的质量推断

IF 7.5 3区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Balázs Pejó;Gergely Biczók
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引用次数: 12

摘要

开发联邦学习算法既是为了提高效率,也是为了确保个人数据和业务数据的隐私性和机密性。尽管没有明确共享数据,但最近的研究表明,这种机制仍然可能泄露敏感信息。因此,在许多实际场景中使用安全聚合来防止归因到特定的参与者。在本文中,我们关注单个训练数据集的质量(即正确标签的比例),并表明即使应用了安全聚合,也可以推断出这种质量信息并归因于特定的参与者。具体来说,通过一系列的图像识别实验,我们推断出参与者的相对质量排序。此外,我们应用推断的质量信息来稳定训练绩效,衡量参与者的个人贡献,并检测不良行为。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Quality Inference in Federated Learning With Secure Aggregation
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.
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来源期刊
CiteScore
11.80
自引率
2.80%
发文量
114
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.
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