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.

翻译：联邦学习算法的开发既出于效率考量，也旨在分别确保个人数据与商业数据的隐私性与机密性。尽管未显式共享数据，但近期研究表明该机制仍可能泄露敏感信息。因此，安全聚合被广泛应用于众多实际场景以防止信息可归因于特定参与者。本文聚焦于个体训练数据集的质量，并证明即使采用安全聚合，此类质量信息仍可被推断并归因于特定参与者。具体而言，通过一系列图像识别实验，我们推断出参与者的相对质量排序。此外，我们将推断出的质量信息应用于异常行为检测、训练性能稳定化及参与者个体贡献度量。