论文标题
LIA：使用懒惰影响近似的联合学习中保存隐私的数据质量评估
LIA: Privacy-Preserving Data Quality Evaluation in Federated Learning Using a Lazy Influence Approximation
论文作者
论文摘要
在联合学习中，处理低质量，损坏或恶意数据至关重要。但是，由于隐私问题，传统的数据评估方法不合适。为了解决这个问题，我们提出了一种简单而有效的方法，该方法利用一种称为“懒惰影响”的新影响近似来过滤和评分数据，同时保留隐私。为此，每个参与者都使用自己的数据来估计另一个参与者的批次的影响，并将差异私有的混淆分数发送给中央协调员。我们的方法已被证明可以在各种模拟和现实世界中成功滤除有偏见和损坏的数据，从而实现$> 90 \％$ $（有时高达$ 100 \％$）的召回率，同时使用$ \ varepsilon \ varepsilon \ leq 1 $保持强大的差异隐私保证。
In Federated Learning, it is crucial to handle low-quality, corrupted, or malicious data. However, traditional data valuation methods are not suitable due to privacy concerns. To address this, we propose a simple yet effective approach that utilizes a new influence approximation called "lazy influence" to filter and score data while preserving privacy. To do this, each participant uses their own data to estimate the influence of another participant's batch and sends a differentially private obfuscated score to the central coordinator. Our method has been shown to successfully filter out biased and corrupted data in various simulated and real-world settings, achieving a recall rate of over $>90\%$ (sometimes up to $100\%$) while maintaining strong differential privacy guarantees with $\varepsilon \leq 1$.
