论文标题
通过安全汇总的联合学习的质量推断
Quality Inference in Federated Learning with Secure Aggregation
论文作者
论文摘要
联合学习算法是出于效率原因而开发的，并分别确保个人和业务数据的隐私和机密性。尽管没有明确共享数据，但最近的研究表明，该机制仍可能泄漏敏感信息。因此，在许多实际情况下使用安全汇总来防止归因于特定参与者。在本文中，我们专注于单个培训数据集的质量，并表明即使应用安全的聚合也可以推断并归因于特定的参与者。具体而言，通过一系列图像识别实验，我们推断了参与者的相对质量排序。此外，我们将推断的质量信息应用于检测不当行为，稳定培训表现并衡量参与者的个人贡献。
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 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 detect misbehaviours, to stabilize training performance, and to measure the individual contributions of participants.
