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• W3138912819 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." @default.
• W3138912819 created "2021-03-29" @default.
• W3138912819 creator A5011355943 @default.
• W3138912819 creator A5030875537 @default.
• W3138912819 date "2023-10-01" @default.
• W3138912819 modified "2023-10-16" @default.
• W3138912819 title "Quality Inference in Federated Learning with Secure Aggregation" @default.
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• W3138912819 doi "https://doi.org/10.1109/tbdata.2023.3280406" @default.
• W3138912819 hasPublicationYear "2023" @default.
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